Calibration Method and Device for LiDAR and Integrated Inertial Navigation

Abstract

A calibration method and device for lidar and integrated inertial navigation. The method includes: obtaining historical data from an unmanned vehicle or a cloud connected to the unmanned vehicle, where the unmanned vehicle includes the lidar and the integrated inertial navigation; extracting data segments that meet the calibration requirements from the historical data; calculating calibration parameters based on the extracted data segments and the historical data, and publishing the calculated calibration parameters to the unmanned vehicle to achieve automatic update of the calibration parameters. The calibration method and device for lidar and integrated inertial navigation automatically extract data segments that meet the calibration requirements from the historical data of the unmanned vehicle for subsequent calibration and update, without the need to specifically collect calibration data. This not only improves the utilization value of historical data, realizes the automatic update of calibration parameters, but also improves the calibration efficiency of lidar and integrated inertial navigation, and reduces the vehicle calibration and operation and maintenance costs.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and more particularly to a calibration method and device for lidar and integrated inertial navigation. Background Art

[0002] With the rapid development of autonomous driving technology, multi-sensor fusion has become the current mainstream sensor configuration solution. Lidar, because it can obtain three-dimensional information of the surrounding environment in real time and has advantages such as high precision, large sensing range, and good stability, has become one of the main sensors of most autonomous vehicles. Integrated inertial navigation can obtain the real-time high-precision pose information of the driving vehicle by combining the global consistency of the global navigation satellite system (GNSS) and the local reliability of the inertial measurement unit (IMU). It is an important part of the current autonomous driving positioning module and an indispensable core part of the autonomous driving high-precision map module.

[0003] The current difficulty in implementing autonomous driving technology lies in the need to adapt to various different types of driving scenarios. The powerful environmental perception ability of lidar and the pose determination and positioning reliability of integrated inertial navigation in different scenarios form a good functional complementarity. The autonomous driving technology that integrates lidar and integrated inertial navigation has become a common research focus in the industrial and academic fields.

[0004] The external parameter calibration technology of lidar and integrated inertial navigation is one of the basic core technologies for the fusion of the two sensors. The main task of calibration is to solve the external parameters of the 3D relative translation and 3D relative rotation between the two sensors, a total of 6 degrees of freedom. The calibration method needs to have high precision and robustness. Due to the influence of factors such as driving vibration, temperature and humidity changes, daily wear and tear of components, and disassembly and assembly, the relative pose between the two sensors will shift over time. Therefore, in order to ensure the fusion accuracy between the two sensors, the calibration results need to be updated regularly, and the calibration task is heavy. How to optimize the existing calibration process and improve the convenience and automation of the calibration method has become a hot research issue in this field.

[0005] According to different calibration scenarios required, the existing lidar and integrated inertial navigation calibration technologies are mainly divided into two categories. The first category is the method based on a high-precision calibration field. This method requires prior construction of a calibration field. First, several strong reflection feature markers are arranged in the scene, and the three-dimensional coordinates of each marker in the integrated inertial navigation world coordinate system are accurately measured based on high-precision measurement technologies such as real-time kinematic carrier phase differential technology (RTK). Thereafter, the lidar captures such markers, and based on this, pose constraints between the two types of sensors are constructed to obtain high-precision calibration results.

[0006] The second category is the calibration method based on outdoor natural scenes, which is currently more widely used in the field of autonomous driving. Two key steps are mainly involved in the calibration process: the first is the acquisition of calibration data. Before calibration, a calibration site with an open view, rich environmental features, and good GNSS signals needs to be found for route design and data collection. The layout of the collection route should try to fully activate each angular axis and each movement direction to obtain effective constraints for each external parameter. Common collection routes include figure-eight, cross, U-shaped, etc.; the second is the solution of calibration parameters. Usually, the relative pose between frames of the multi-line lidar can be calculated using lidar odometry or SLAM technology, and combined with the pose information provided by the integrated inertial navigation, the external parameters are solved based on methods such as hand-eye calibration.

[0007] Although the calibration method based on natural scenes has many advantages such as low calibration cost, simple operation, and relatively high degree of automation compared with the calibration field method. However, this type of method still requires a lot of manual intervention, especially in the stages of scene selection, route design, and data collection. Each time the calibration parameters are updated, this calibration process needs to be repeated. Especially for unmanned vehicles operating on a large scale, the manual intervention in the calibration process seriously affects the operation and maintenance cost and operation efficiency of the unmanned vehicle. Summary of the Invention

[0008] This application is proposed to solve the above problems. According to one aspect of this application, a calibration method for a lidar and an integrated inertial navigation is provided. The method includes: obtaining historical data from an unmanned vehicle or a cloud connected to the unmanned vehicle, where the unmanned vehicle includes the lidar and the integrated inertial navigation; extracting data segments that meet the calibration requirements from the historical data; performing calibration parameter calculation based on the extracted data segments and the historical data, and publishing the calculated calibration parameters to the unmanned vehicle to achieve automatic update of the calibration parameters.

[0009] In an embodiment of this application, the historical data includes the integrated inertial navigation pose data and lidar point cloud data recorded in the most recent time period and the most recent historical calibration parameter file; the extracting data segments that meet the calibration requirements from the historical data includes: extracting data segments that meet the calibration requirements from the integrated inertial navigation pose data; the performing calibration parameter calculation based on the extracted data segments and the historical data includes: performing calibration parameter calculation based on the extracted data segments, the lidar point cloud data, and the historical calibration parameter file.

[0010] In an embodiment of this application, the extracting data segments that meet the calibration requirements from the integrated inertial navigation pose data includes: extracting a set of fixed-length U-shaped trajectory segments with global satellite navigation positioning system signals meeting preset conditions from the integrated inertial navigation pose data.

[0011] In one embodiment of the present application, the extraction of the fixed-length U-shaped trajectory segment set includes: The first step: Traverse the combined inertial navigation pose data, and calculate the cumulative travel distance from each pose point in the combined inertial navigation pose data to the starting point; The second step: Starting from the starting point, sequentially intercept the pose trajectory segment with the current pose point as the starting point and another pose point as the ending point, where the cumulative travel distances of the current pose point and the other pose point to the starting point satisfy a preset relationship; The third step: Determine whether the pose trajectory segment is a U-shaped trajectory segment: If it is a U-shaped trajectory segment, add the pose trajectory segment to the segment set, and jump to the second step to traverse the pose points subsequent to the other pose point; If it is not a U-shaped trajectory segment, jump to the second step to traverse the pose points subsequent to the current pose point; until the traversal ends; The fourth step: Calculate the global satellite navigation positioning system signal mean value of each pose trajectory segment in the segment set, and sort all the pose trajectory segments in descending order according to the mean value, and select the preset number of pose trajectory segments with the top ranking as the extraction result of the fixed-length U-shaped trajectory segment set.

[0012] In one embodiment of the present application, determining whether the pose trajectory segment is a U-shaped trajectory segment includes: determining whether the pose trajectory segment satisfies three-dimensional conditions, and when the three-dimensional conditions are satisfied, determining that the pose trajectory segment is a U-shaped trajectory segment; wherein, the three-dimensional conditions include: the difference between the yaw angles corresponding to the current pose point and the other pose point is within a preset angle range; the line segment connecting the current pose point as the starting point and the other pose point as the ending point is approximately perpendicular to the line segment connecting the current pose point as the starting point and the next adjacent pose point of the current pose point as the ending point; the straight-line distance between the current pose point and the other pose point is less than a preset distance threshold.

[0013] In one embodiment of the present application, the preset angle range is from 170 degrees to 190 degrees.

[0014] In one embodiment of the present application, the value range of the preset distance threshold is from 1 meter to 8 meters.

[0015] In one embodiment of the present application, the cumulative travel distances of the current pose point and the other pose point to the starting point satisfy a preset relationship, including: adding the preset trajectory segment length to the cumulative travel distance from the current pose point to the starting point as the first distance, taking the cumulative travel distance from the other pose point to the starting point as the second distance, and taking the cumulative travel distance from the next adjacent pose point of the other pose point to the starting point as the third distance, the second distance is less than the first distance, and the first distance is less than the third distance.

[0016] In an embodiment of the present application, the global satellite navigation and positioning system signal meets a preset condition, including: the signal strength of the global satellite navigation and positioning system signal is greater than or equal to a preset signal strength threshold.

[0017] In an embodiment of the present application, the calibration parameter calculation based on the extracted data segment, the lidar point cloud data, and the historical calibration parameter file includes: extracting local point cloud data corresponding to the timestamp of the data segment from the lidar point cloud data, and based on the local point cloud data and the data segment, using the historical calibration parameters in the historical calibration parameter file as the initial value to calculate new calibration parameters.

[0018] According to another aspect of the present application, there is provided a calibration device for a lidar and an integrated inertial navigation system. The device includes a memory and a processor. A computer program is stored on the memory and run by the processor. When the computer program is run by the processor, the processor performs the following operations: obtaining historical data from an unmanned vehicle or a cloud connected to the unmanned vehicle, where the unmanned vehicle includes the lidar and the integrated inertial navigation system; extracting data segments that meet the calibration requirements from the historical data; performing calibration parameter calculation based on the extracted data segments and the historical data, and publishing the calculated calibration parameters to the unmanned vehicle to achieve automatic update of the calibration parameters.

[0019] In an embodiment of the present application, the historical data includes the integrated inertial navigation system pose data and the lidar point cloud data recorded in the most recent time period and the most recent historical calibration parameter file; the processor extracts data segments that meet the calibration requirements from the historical data, including: extracting data segments that meet the calibration requirements from the integrated inertial navigation system pose data; the processor performs calibration parameter calculation based on the extracted data segments and the historical data, including: performing calibration parameter calculation based on the extracted data segments, the lidar point cloud data, and the historical calibration parameter file.

[0020] In an embodiment of the present application, the processor extracts data segments that meet the calibration requirements from the integrated inertial navigation system pose data, including: extracting a set of fixed-length U-shaped trajectory segments where the global satellite navigation and positioning system signal meets the preset condition from the integrated inertial navigation system pose data.

[0021] In one embodiment of the present application, the extraction of the fixed-length U-shaped trajectory segment set by the processor includes: First step: Traverse the combined inertial navigation pose data, and calculate the cumulative driving distance from each pose point in the combined inertial navigation pose data to the starting point; Second step: Starting from the starting point, sequentially intercept pose trajectory segments with the current pose point as the starting point and another pose point as the ending point, where the cumulative driving distances of the current pose point and the other pose point to the starting point satisfy a preset relationship; Third step: Determine whether the pose trajectory segment is a U-shaped trajectory segment: If it is a U-shaped trajectory segment, add the pose trajectory segment to the segment set, and jump to the second step to traverse the pose points subsequent to the other pose point; If it is not a U-shaped trajectory segment, jump to the second step to traverse the pose points subsequent to the current pose point; until the traversal ends; Fourth step: Calculate the global satellite navigation positioning system signal mean value of each pose trajectory segment in the segment set, and sort all the pose trajectory segments in descending order according to the mean value, and select a preset number of the pose trajectory segments with the top ranking as the extraction result of the fixed-length U-shaped trajectory segment set.

[0022] In one embodiment of the present application, the processor determines whether the pose trajectory segment is a U-shaped trajectory segment, including: determining whether the pose trajectory segment satisfies three-dimensional conditions, and when the three-dimensional conditions are satisfied, determining that the pose trajectory segment is a U-shaped trajectory segment; wherein, the three-dimensional conditions include: the difference between the yaw angles corresponding to the current pose point and the other pose point is within a preset angle range; the line segment connecting the current pose point as the starting point and the other pose point as the ending point is approximately perpendicular to the line segment connecting the current pose point as the starting point and the next adjacent pose point of the current pose point as the ending point; the straight-line distance between the current pose point and the other pose point is less than a preset distance threshold.

[0023] In one embodiment of the present application, the preset angle range is from 170 degrees to 190 degrees.

[0024] In one embodiment of the present application, the value range of the preset distance threshold is from 1 meter to 8 meters.

[0025] In one embodiment of the present application, the cumulative driving distances of the current pose point and the other pose point to the starting point satisfy a preset relationship, including: adding a preset trajectory segment length to the cumulative driving distance from the current pose point to the starting point as the first distance, taking the cumulative driving distance from the other pose point to the starting point as the second distance, taking the cumulative driving distance from the next adjacent pose point of the other pose point to the starting point as the third distance, the second distance is less than the first distance, and the first distance is less than the third distance.

[0026] In one embodiment of the present application, the global satellite navigation and positioning system signal satisfies a preset condition, including: the signal strength of the global satellite navigation and positioning system signal is greater than or equal to a preset signal strength threshold.

[0027] In one embodiment of the present application, the processor performs calibration parameter calculation based on the extracted data segment, the lidar point cloud data, and the historical calibration parameter file, including: extracting local point cloud data corresponding to the timestamp of the data segment from the lidar point cloud data, and based on the local point cloud data and the data segment, using the historical calibration parameters in the historical calibration parameter file as the initial value to calculate new calibration parameters.

[0028] According to another aspect of the present application, a system for autonomous driving is provided. The system includes a positioning subsystem, a perception subsystem, a decision-making subsystem, and a control subsystem, where: the positioning subsystem is used to obtain the pose information of the autonomous driving vehicle in real time and transmit it to the decision-making subsystem; the perception subsystem is used to detect lanes and obstacles and transmit the detection results to the decision-making subsystem; the decision-making subsystem is used to make decisions on the autonomous driving vehicle by combining the data information transmitted by the positioning subsystem and the perception subsystem and transmit the decision information to the control subsystem; the control subsystem is used to control the autonomous driving vehicle based on the decision information transmitted by the decision-making subsystem; where the positioning subsystem includes the above-mentioned calibration device for lidar and integrated inertial navigation to perform calibration of lidar and integrated inertial navigation.

[0029] According to yet another aspect of the present application, a storage medium is provided. A computer program run by a processor is stored on the storage medium. When the computer program is run by the processor, the processor is caused to execute the above-mentioned calibration method for lidar and integrated inertial navigation.

[0030] The calibration method and device for lidar and integrated inertial navigation according to the embodiments of the present application automatically extract data segments that meet the calibration requirements from the historical data of the unmanned vehicle for subsequent calibration and update, without the need to specially collect calibration data, which not only improves the utilization value of historical data, realizes the automatic update of calibration parameters, but also improves the calibration efficiency of lidar and integrated inertial navigation and reduces the vehicle calibration and operation and maintenance costs. Description of the Drawings

[0031] The above and other objects, features, and advantages of the present application will become more apparent by describing the embodiments of the present application in more detail with reference to the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. They are used to explain the present application together with the embodiments of the present application and do not constitute a limitation to the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0032] Figure 1 A schematic block diagram of an exemplary electronic device for implementing a calibration method and apparatus for a lidar and an integrated inertial navigation system according to an embodiment of the present invention is shown.

[0033] Figure 2 A schematic flowchart of a calibration method for a lidar and an integrated inertial navigation system according to an embodiment of the present application is shown.

[0034] Figure 3 A more detailed schematic flowchart of a calibration method for a lidar and an integrated inertial navigation system according to an embodiment of the present application is shown.

[0035] Figure 4 A schematic structural block diagram of a calibration apparatus for a lidar and an integrated inertial navigation system according to an embodiment of the present application is shown.

[0036] Figure 5 A schematic structural block diagram of a system for autonomous driving according to an embodiment of the present application is shown. Detailed implementation manners

[0037] In order to make the objectives, technical solutions, and advantages of the present application more apparent, exemplary embodiments according to the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described herein. Based on the embodiments of the present application described herein, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0038] First, refer to Figure 1 to describe an exemplary electronic device 100 for implementing a calibration method and apparatus for a lidar and an integrated inertial navigation system according to an embodiment of the present invention.

[0039] As Figure 1 shown, the electronic device 100 includes one or more processors 102, one or more storage devices 104, an input device 106, and an output device 108. These components are interconnected through a bus system 110 and / or other forms of connection mechanisms (not shown). It should be noted that Figure 1The components and structures of the electronic device 100 shown are exemplary and not restrictive. As needed, the electronic device may also have other components and structures.

[0040] The processor 102 may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 100 to perform desired functions.

[0041] The storage device 104 may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 102 may run the program instructions to implement the client functions (implemented by the processor) in the embodiments of the present invention described below and / or other desired functions. Various application programs and various data may also be stored in the computer-readable storage medium, such as various data used and / or generated by the application programs, etc.

[0042] The input device 106 may be a device used by a user to input instructions, and may include one or more of a keyboard, a mouse, a microphone, a touch screen, etc. In addition, the input device 106 may also be any interface for receiving information.

[0043] The output device 108 may output various information (such as images or sounds) to the outside (such as a user), and may include one or more of a display, a speaker, etc. In addition, the output device 108 may also be any other device with output functions.

[0044] Exemplarily, an example electronic device for implementing the calibration method and device of lidar and integrated inertial navigation according to the embodiments of the present invention may be implemented as, for example, an intelligent vehicle terminal, etc.

[0045] Next, reference will be made to Figure 2 Describe the calibration method 200 of lidar and integrated inertial navigation according to the embodiments of the present application. As Figure 2 shown, the calibration method 200 of lidar and integrated inertial navigation may include the following steps:

[0046] In step S210, historical data is obtained from an unmanned vehicle or a cloud connected to the unmanned vehicle, and the unmanned vehicle includes a lidar and an integrated inertial navigation.

[0047] In step S220, data segments that meet the calibration requirements are extracted from the historical data.

[0048] In step S230, calibration parameters are calculated based on the extracted data segments and the historical data, and the calculated calibration parameters are published to the driverless vehicle to achieve automatic update of the calibration parameters.

[0049] In an embodiment of the present application, the calibration method 200 for lidar and integrated inertial navigation provides a method for automatically calculating and updating the calibration parameters of lidar and integrated inertial navigation. Specifically, the calibration method 200 for lidar and integrated inertial navigation does not require special collection of calibration data, but automatically extracts data segments that meet the calibration requirements from the daily historical data obtained by the driverless operation vehicle or the full-scale road data recorded by the high-precision map collection vehicle for subsequent calibration and update. Its main advantages are as follows: on the one hand, since the entire calibration process is automated and unmanned, and the calibration data is directly extracted from the historical operation data, the calibration cost tends to zero; on the other hand, since the calibration parameters can be continuously updated with the vehicle operation, compared with the periodic or irregular manual update of the traditional calibration method, the update frequency has obvious advantages, so that the real-time accuracy of the calibration parameters can be guaranteed.

[0050] In an embodiment of the present application, the historical data obtained in step S210 may include the full-scale integrated inertial navigation pose data and lidar 3D point cloud data recorded in the most recent time period and the most recent historical calibration parameter file. Among them, these three types of data may have a fixed format and a unified naming convention, which is convenient for subsequent data parsing and parameter calibration. Based on the above historical data, the extraction of data segments that meet the calibration requirements from the historical data in step S220 may include: extracting data segments that meet the calibration requirements from the integrated inertial navigation pose data. Correspondingly, the calculation of calibration parameters based on the extracted data segments and the historical data in step S230 may include: calculating calibration parameters based on the extracted data segments, lidar point cloud data, and historical calibration parameter file.

[0051] In an embodiment of the present application, extracting data segments that meet the calibration requirements from the combined inertial navigation pose data may include: extracting a set of fixed-length U-shaped trajectory segments from the combined inertial navigation pose data where the Global Navigation Satellite System (GNSS) signals meet preset conditions. Here, the set of fixed-length U-shaped trajectory segments where the GNSS signals meet preset conditions may refer to: a set of fixed-length U-shaped trajectory segments with good GNSS signals (for example, the signal strength is greater than or equal to a preset signal strength threshold). In this embodiment, a set of data segments that can be used for calibration is extracted from the full-volume combined inertial navigation pose data. Specifically, in combination with the actual driving trajectory of the vehicle and the specific requirements of the existing calibration algorithm for calibration data, the extraction of the set of U-shaped trajectory segments is used as an example for description. It should be understood that it is also possible to extract 8-shaped, cross-shaped, or other-shaped trajectory segments from the combined inertial navigation pose data for subsequent calibration parameter calculation.

[0052] The extraction process of the set of fixed-length U-shaped trajectory segments is described in detail below. In an embodiment of the present application, the extraction process of the set of fixed-length U-shaped trajectory segments may include the following first step to the fourth step:

[0053] First step: Traverse the combined inertial navigation pose data and calculate the cumulative driving distance of each pose point in the combined inertial navigation pose data to the starting point. Assuming that the set of combined inertial navigation pose data is represented as X, then the cumulative driving distance s i from each pose point x i in the set X to the starting point x0 can be calculated.

[0054] Second step: Starting from the starting point, sequentially intercept pose trajectory segments with the current pose point as the starting point and another pose point as the ending point, where the cumulative driving distances of the current pose point and this other pose point to the starting point satisfy a preset relationship. Combining the symbolic representation in the first step, that is: Starting from the starting point x0, sequentially intercept the pose trajectory segment [x i as the starting point and x j as the ending point. In an example, the cumulative driving distances corresponding to the two pose points x i and x j can satisfy the following relationship: s i < s j + L < s j < s i + L < s j+1, where L is the length of the pre-set trajectory segment. For example, the value of L can be 100 meters. That is, the cumulative travel distances of the current pose point and the other pose point from the starting point satisfy a preset relationship, which may include: taking the cumulative travel distance of the current pose point from the starting point plus the pre-set trajectory segment length as the first distance, taking the cumulative travel distance of the other pose point from the starting point as the second distance, taking the cumulative travel distance of the next adjacent pose point of the other pose point from the starting point as the third distance, where the second distance is less than the first distance and the first distance is less than the third distance.

[0055] The third step: Determine whether the pose trajectory segment is a U-shaped trajectory segment: If it is a U-shaped trajectory segment, add the pose trajectory segment to the segment set and jump to the second step to traverse the subsequent pose points of the other pose point; if it is not a U-shaped trajectory segment, jump to the second step to traverse the subsequent pose points of the current pose point; until the traversal ends. Combining the symbolic representations in the first step and the second step, it is to determine whether the pose trajectory segment [x i , x j is a U-shaped trajectory segment: If it is a U-shaped trajectory segment, add the pose trajectory segment [x i , x j to the segment set U and jump to the second step to traverse the subsequent pose points of the other pose point x j ; if it is not a U-shaped trajectory segment, jump to the second step to traverse the subsequent pose points of the current pose point x i ; until the traversal ends.

[0056] Among them, in the embodiments of the present application, determining whether the pose trajectory segment is a U-shaped trajectory segment may include: determining whether the pose trajectory segment satisfies three-dimensional conditions, and when the three-dimensional conditions are satisfied, determining that the pose trajectory segment is a U-shaped trajectory segment; where the three-dimensional conditions include: the difference between the respective yaw angles corresponding to the current pose point and the other pose point is within a preset angle range; the line segment connecting the current pose point as the starting point and the other pose point as the end point is approximately perpendicular to the line segment connecting the current pose point as the starting point and the next adjacent pose point of the current pose point as the end point; the straight-line distance between the current pose point and the other pose point is less than a preset distance threshold.

[0057] Combining the symbolic representations in the first step and the second step, the three-dimensional conditions are: the difference between the respective yaw angles of the current pose point x i and the other pose point x j is within a preset angle range; the line segment connecting the current pose point x i as the starting point and the other pose point x j as the end point is approximately perpendicular to the line segment connecting the current pose point x i as the starting point and the current pose point xi The next adjacent pose point x i+1 is the line segment formed by connecting to the end point; the current pose point x i and the other pose point x j The straight-line distance between them is less than the preset distance threshold θ. In one example, the aforementioned preset angle range can be [180 - ε, 180 + ε], where ε is the angle tolerance. For example, ε can be taken as 10 degrees, then the preset angle range is from 170 degrees to 190 degrees. In one example, the value range of the aforementioned preset distance threshold can be 1 < θ < 8, that is, the value range of the preset distance threshold is from 1 meter to 8 meters. In addition, "approximately perpendicular" can be understood as that the angle between two line segments is close to 90 degrees to be considered to meet the condition. For example, the angle between two line segments can be [90 - α, 90 + α], α is the angle tolerance, for example, α can be taken as 5 degrees or other appropriate degrees.

[0058] Fourth step: Calculate the global satellite navigation positioning system signal mean value of each pose trajectory segment in the segment set, and sort all pose trajectory segments in descending order of the mean value, and select the preset number of pose trajectory segments with the top ranking as the extraction result of the fixed-length U-shaped trajectory segment set. Combining the symbolic representations in the first step, the second step, and the third step, it is: Calculate the GNSS signal mean value corresponding to each trajectory segment in the set U, and sort the trajectory segments based on this, and select the top N segments as the final result for subsequent parameter calibration. Among them, the value of N is set according to requirements.

[0059] Based on the extracted data segments, the calibration parameter calculation and update can be performed. In the embodiments of the present application, the aforementioned calculation of calibration parameters based on the extracted data segments, lidar point cloud data, and historical calibration parameter files may include: Extracting the local point cloud data corresponding to the time stamp of the data segment from the lidar point cloud data, and based on the local point cloud data and the data segment, using the historical calibration parameters in the historical calibration parameter file as the initial value, calculating the new calibration parameters. Specifically, taking the U-shaped trajectory segment set extracted in the previous step as the input, first extract the local point cloud data corresponding to the trajectory segment time stamp from the full-scale lidar point cloud data, and then use the historical calibration parameters as the initial value, and perform the calculation and refinement of the calibration parameters through existing hand-eye calibration or other various calibration algorithms. Finally, by publishing the newly calculated calibration parameters to the vehicle end, the automatic update of the calibration parameters is realized.

[0060] It can be combined with Figure 3 to better understand the above details. Generally, as Figure 3As shown in the figure, the calibration method of the lidar and the integrated inertial navigation in the present application can continuously update the calibration parameters of the lidar and the integrated inertial navigation by repeatedly executing the steps of "historical data collection", "extraction of calibration data trajectory segments", "solution of calibration parameters", and "update of calibration parameters".

[0061] Based on the above description, the calibration method of the lidar and the integrated inertial navigation according to the embodiments of the present application automatically extracts data segments that meet the calibration requirements from the historical data of the driverless vehicle for subsequent calibration and update, without specially collecting calibration data. This not only improves the utilization value of the historical data, realizes the automatic update of the calibration parameters, but also improves the calibration efficiency of the lidar and the integrated inertial navigation, and reduces the vehicle calibration and operation and maintenance costs.

[0062] The calibration method of the lidar and the integrated inertial navigation according to the embodiments of the present application is described above by way of example. The following will be combined with Figure 4 to describe the calibration device of the lidar and the integrated inertial navigation provided in another aspect of the present application. Figure 4 FIG. shows a schematic block diagram of a calibration device 400 for a lidar and an integrated inertial navigation according to an embodiment of the present application. As Figure 4 shown, the calibration device 400 for a lidar and an integrated inertial navigation according to an embodiment of the present application may include a memory 410 and a processor 420. The memory 410 stores a computer program run by the processor 420. When the computer program is run by the processor 420, the processor 420 is caused to execute the calibration method of the lidar and the integrated inertial navigation according to the embodiments of the present application described above. Those skilled in the art can understand the specific operations of the calibration device of the lidar and the integrated inertial navigation according to the embodiments of the present application in combination with the content described above. For the sake of brevity, the specific details will not be described here, and only some main operations of the processor 420 will be described.

[0063] In an embodiment of the present application, when the computer program is run by the processor 420, the processor 420 is caused to execute the following steps: obtaining historical data from a driverless vehicle or a cloud connected to the driverless vehicle, where the driverless vehicle includes a lidar and an integrated inertial navigation; extracting data segments that meet the calibration requirements from the historical data; performing calibration parameter calculation based on the extracted data segments and the historical data, and publishing the calculated calibration parameters to the driverless vehicle to realize automatic update of the calibration parameters.

[0064] In one embodiment of the present application, the historical data includes the combined inertial navigation pose data and lidar point cloud data recorded within the most recent time period, as well as the most recent historical calibration parameter file; when the computer program is run by the processor 420, it causes the processor 420 to extract data segments that meet the calibration requirements from the historical data, including: extracting data segments that meet the calibration requirements from the combined inertial navigation pose data; when the computer program is run by the processor 420, it causes the processor 420 to perform calibration parameter calculation based on the extracted data segments and the historical data, including: performing calibration parameter calculation based on the extracted data segments, lidar point cloud data, and historical calibration parameter file.

[0065] In one embodiment of the present application, when the computer program is run by the processor 420, it causes the processor 420 to extract data segments that meet the calibration requirements from the combined inertial navigation pose data, including: extracting a set of fixed-length U-shaped trajectory segments from the combined inertial navigation pose data where the global satellite navigation positioning system signals meet preset conditions.

[0066] In one embodiment of the present application, when the computer program is run by the processor 420, the extraction of the set of fixed-length U-shaped trajectory segments by the processor 420 includes: First step: Traverse the combined inertial navigation pose data and calculate the cumulative travel distance of each pose point in the combined inertial navigation pose data to the starting point; Second step: Starting from the starting point, sequentially intercept a pose trajectory segment with the current pose point as the starting point and another pose point as the ending point, where the cumulative travel distances of the current pose point and this other pose point to the starting point satisfy a preset relationship; Third step: Determine whether the pose trajectory segment is a U-shaped trajectory segment: If it is a U-shaped trajectory segment, add the pose trajectory segment to the segment set and jump to the second step to traverse the pose points subsequent to this other pose point; If it is not a U-shaped trajectory segment, jump to the second step to traverse the pose points subsequent to the current pose point; Repeat until the traversal ends; Fourth step: Calculate the global satellite navigation positioning system signal mean of each pose trajectory segment in the segment set, and sort all the pose trajectory segments in descending order according to the mean, and select the preset number of pose trajectory segments with the highest rankings as the extraction result of the set of fixed-length U-shaped trajectory segments.

[0067] In one embodiment of the present application, when the computer program is run by the processor 420, determining whether the pose trajectory segment is a U-shaped trajectory segment, which the processor 420 executes, includes: determining whether the pose trajectory segment meets the three-dimensional condition, and when the three-dimensional condition is met, determining that the pose trajectory segment is a U-shaped trajectory segment; wherein, the three-dimensional condition includes: the difference between the yaw angles corresponding to the current pose point and the other pose point is within a preset angle range; the line segment connecting the current pose point as the starting point and the other pose point as the ending point is approximately perpendicular to the line segment connecting the current pose point as the starting point and the next adjacent pose point of the current pose point as the ending point; the straight-line distance between the current pose point and the other pose point is less than a preset distance threshold.

[0068] In one embodiment of the present application, the preset angle range is from 170 degrees to 190 degrees.

[0069] In one embodiment of the present application, the value range of the preset distance threshold is from 1 meter to 8 meters.

[0070] In one embodiment of the present application, the cumulative driving distances of the current pose point and the other pose point to the starting point satisfy a preset relationship, including: adding the preset trajectory segment length to the cumulative driving distance of the current pose point to the starting point as the first distance, taking the cumulative driving distance of the other pose point to the starting point as the second distance, taking the cumulative driving distance of the next adjacent pose point of the other pose point to the starting point as the third distance, the second distance is less than the first distance, and the first distance is less than the third distance.

[0071] In one embodiment of the present application, the global satellite navigation and positioning system signal meets the preset conditions, including: the signal strength of the global satellite navigation and positioning system signal is greater than or equal to a preset signal strength threshold.

[0072] In one embodiment of the present application, when the computer program is run by the processor 420, the processor 420 executes the calibration parameter calculation based on the extracted data segment, the lidar point cloud data, and the historical calibration parameter file, including: extracting the local point cloud data corresponding to the timestamp of the data segment from the lidar point cloud data, and based on the local point cloud data and the data segment, using the historical calibration parameters in the historical calibration parameter file as the initial values to calculate new calibration parameters.

[0073] Based on the above description, the calibration device of the lidar and the integrated inertial navigation according to the embodiments of the present application automatically extracts the data segments that meet the calibration requirements from the historical data of the unmanned vehicle for subsequent calibration and update, without specially collecting calibration data, which not only improves the utilization value of the historical data, realizes the automatic update of the calibration parameters, but also improves the calibration efficiency of the lidar and the integrated inertial navigation, and reduces the vehicle calibration and operation and maintenance costs.

[0074] In addition, according to an embodiment of the present application, a storage medium is further provided. Program instructions are stored on the storage medium, and when the program instructions are run by a computer or a processor, they are used to execute the corresponding steps of the calibration method for lidar and integrated inertial navigation according to the embodiment of the present application. The storage medium may include, for example, a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0075] In addition, a computer program is further provided, and when the computer program is run by a computer or a processor, it is used to execute the corresponding steps of the calibration method for lidar and integrated inertial navigation according to the embodiment of the present application.

[0076] According to another aspect of the present application, a system for autonomous driving is further provided, which will be described below in conjunction with Figure 5 for description. Figure 5 FIG. shows a schematic structural block diagram of a system 500 for autonomous driving according to an embodiment of the present application. As Figure 5 shown, the system 500 for autonomous driving includes a positioning subsystem 510, a perception subsystem 520, a decision-making subsystem 530, and a control subsystem 540. Among them, the positioning subsystem 510 is used to obtain the pose information of the autonomous driving vehicle in real time and transmit it to the decision-making subsystem 530; the perception subsystem 520 is used to detect lanes and obstacles and transmit the detection results to the decision-making subsystem 530; the decision-making subsystem 530 is used to make decisions on the autonomous driving vehicle by combining the data information transmitted by the positioning subsystem 510 and the perception subsystem 520 and transmit the decision-making information to the control subsystem 540; the control subsystem 540 is used to control the autonomous driving vehicle based on the decision-making information transmitted by the decision-making subsystem 530; among them, the positioning subsystem 510 includes the lidar and integrated inertial navigation calibration device 400 according to the embodiment of the present application described above to execute the lidar and integrated inertial navigation calibration method 200 according to the embodiment of the present application described above for performing the calibration of lidar and integrated inertial navigation. Those skilled in the art can understand the specific operations of the decision-making subsystem for path planning in the system for autonomous driving according to the embodiment of the present application in combination with the content described above. For the sake of brevity, it will not be elaborated here.

[0077] Based on the above description, the calibration method and device for lidar and integrated inertial navigation according to the embodiments of the present application, as well as the system for autonomous driving, automatically extract data segments that meet the calibration requirements from the historical data of the driverless vehicle for subsequent calibration and update, without the need to specially collect calibration data. This not only improves the utilization value of historical data, realizes the automatic update of calibration parameters, but also improves the calibration efficiency of lidar and integrated inertial navigation, and reduces the vehicle calibration and operation and maintenance costs.

[0078] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely exemplary and are not intended to limit the scope of the present application. Those of ordinary skill in the art can make various changes and modifications therein without departing from the scope and spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as claimed in the appended claims.

[0079] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0080] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0081] In the specification provided herein, a large number of specific details are set forth. However, it can be understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures, and technologies have not been shown in detail so as not to obscure the understanding of this specification.

[0082] Similarly, it should be understood that, for the purpose of streamlining the present application and assisting in understanding one or more of the various inventive aspects, in the description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the method of the present application should not be construed as reflecting an intention that the claimed present application requires more features than those expressly recited in each claim. Rather, as reflected by the corresponding claims, the inventive point lies in that the corresponding technical problem can be solved with features fewer than all the features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into the detailed description, where each claim itself serves as a separate embodiment of the present application.

[0083] Those skilled in the art will appreciate that, except where features are mutually exclusive, any combination can be used to combine all the features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all the processes or units of any method or device so disclosed. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) can be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0084] In addition, those skilled in the art will be able to understand that, although some of the embodiments described herein include certain features included in other embodiments rather than other features, the combination of features of different embodiments means that it is within the scope of the present application and forms different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0085] The various component embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some of the modules according to the embodiments of the present application. The present application can also be implemented as a device program (such as a computer program and a computer program product) for executing part or all of the methods described herein. Such a program for implementing the present application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, or provided on a carrier signal, or in any other form.

[0086] It should be noted that the above embodiments are illustrative of the present application rather than restrictive of the present application, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in the claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim listing several devices, several of these devices may be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not denote any order. These words may be interpreted as names.

[0087] As described above, the above is only the specific implementation manner of the present application or the description of the specific implementation manner, and the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should be covered by the protection scope of the present application. The protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A calibration method for lidar and integrated inertial navigation, characterized in that The method includes: Obtaining historical data from an autonomous vehicle or a cloud connected to the autonomous vehicle, where the autonomous vehicle includes the lidar and the integrated inertial navigation; Extracting data segments that meet the calibration requirements from the historical data; Calculating calibration parameters based on the extracted data segments and the historical data, and publishing the calculated calibration parameters to the autonomous vehicle to achieve automatic update of the calibration parameters; Wherein, the historical data includes integrated inertial navigation pose data and lidar point cloud data recorded within the most recent time period and the most recent historical calibration parameter file; The extracting data segments that meet the calibration requirements from the historical data includes: extracting data segments that meet the calibration requirements from the integrated inertial navigation pose data; The calculating calibration parameters based on the extracted data segments and the historical data includes: extracting local point cloud data corresponding to the time stamp of the data segments from the lidar point cloud data, and calculating new calibration parameters based on the local point cloud data and the data segments, with the historical calibration parameters in the historical calibration parameter file as the initial values.

2. The method according to claim 1, wherein The extracting data segments that meet the calibration requirements from the integrated inertial navigation pose data includes: Extracting a set of fixed-length U-shaped trajectory segments from the integrated inertial navigation pose data where the global navigation satellite system signal meets a preset condition.

3. The method according to claim 2, wherein The extraction of the set of fixed-length U-shaped trajectory segments includes: First step: Traverse the integrated inertial navigation pose data and calculate the cumulative driving distance of each pose point in the integrated inertial navigation pose data to the starting point; Second step: Sequentially intercept a pose trajectory segment starting from the current pose point and ending at another pose point, where the cumulative driving distances of the current pose point and the other pose point to the starting point satisfy a preset relationship; Third step: Determine whether the pose trajectory segment is a U-shaped trajectory segment: If it is a U-shaped trajectory segment, add the pose trajectory segment to the segment set, and jump to the second step to traverse the pose points after the other pose point; If it is not a U-shaped trajectory segment, jump to the second step to traverse the pose points after the current pose point; until the traversal ends; Fourth step: Calculate the global navigation satellite system signal mean value of each pose trajectory segment in the segment set, and sort all the pose trajectory segments in descending order according to the mean value, and select a preset number of the pose trajectory segments with the highest ranking as the extraction result of the set of fixed-length U-shaped trajectory segments.

4. The method according to claim 3, wherein The determining whether the pose trajectory segment is a U-shaped trajectory segment includes: Determining whether the pose trajectory segment meets three-dimensional conditions, and when the three-dimensional conditions are met, determining that the pose trajectory segment is a U-shaped trajectory segment; Wherein, the three-dimensional conditions include: The difference between the yaw angles corresponding to the current pose point and the other pose point is within a preset angle range; The line segment connecting the current pose point as the starting point and the other pose point as the ending point is approximately perpendicular to the line segment connecting the current pose point as the starting point and the next adjacent pose point of the current pose point as the ending point; The straight-line distance between the current pose point and the other pose point is less than a preset distance threshold.

5. The method according to claim 4, wherein The preset angle range is from 170 degrees to 190 degrees.

6. The method according to claim 4, wherein The value range of the preset distance threshold is from 1 meter to 8 meters.

7. The method according to claim 3, wherein The cumulative travel distances of the current pose point and the other pose point to the starting point satisfy a preset relationship, including: Adding the preset trajectory segment length to the cumulative travel distance of the current pose point to the starting point as the first distance, taking the cumulative travel distance of the other pose point to the starting point as the second distance, and taking the cumulative travel distance of the next adjacent pose point of the other pose point to the starting point as the third distance. The second distance is less than the first distance, and the first distance is less than the third distance.

8. The method according to claim 2, wherein The global satellite navigation and positioning system signal satisfies preset conditions, including: The signal strength of the global satellite navigation and positioning system signal is greater than or equal to a preset signal strength threshold.

9. A calibration device for a lidar and an integrated inertial navigation system, characterized in that, The device includes a memory and a processor. A computer program is stored on the memory and run by the processor. When the computer program is run by the processor, the processor performs the following operations: Obtain historical data from the unmanned vehicle or the cloud linked to the unmanned vehicle. The unmanned vehicle includes the lidar and the integrated inertial navigation; Extract data segments that meet the calibration requirements from the historical data; Perform calibration parameter calculation based on the extracted data segments and the historical data, and publish the calculated calibration parameters to the unmanned vehicle to achieve automatic update of the calibration parameters; Among them, the historical data includes the integrated inertial navigation pose data and lidar point cloud data recorded in the most recent time period and the most recent historical calibration parameter file; The processor extracts data segments that meet the calibration requirements from the historical data, including: extracting data segments that meet the calibration requirements from the integrated inertial navigation pose data; The processor performs calibration parameter calculation based on the extracted data segments and the historical data, including: extracting local point cloud data corresponding to the time stamp of the data segment from the lidar point cloud data, and based on the local point cloud data and the data segment, using the historical calibration parameters in the historical calibration parameter file as the initial value to calculate new calibration parameters.

10. The device according to claim 9, characterized in that, The processor extracts data segments that meet the calibration requirements from the integrated inertial navigation pose data, including: Extracting a set of fixed-length U-shaped trajectory segments whose global satellite navigation and positioning system signals meet preset conditions from the integrated inertial navigation pose data.

11. The device according to claim 10, characterized in that, The processor's extraction of the set of fixed-length U-shaped trajectory segments includes: The first step: Traverse the integrated inertial navigation pose data and calculate the cumulative travel distance of each pose point in the integrated inertial navigation pose data to the starting point; Second step: Starting from the starting point, sequentially intercept a pose trajectory segment with the current pose point as the starting point and another pose point as the ending point, where the cumulative driving distances of the current pose point and the other pose point to the starting point satisfy a preset relationship; Third step: Determine whether the pose trajectory segment is a U-shaped trajectory segment: If it is a U-shaped trajectory segment, add the pose trajectory segment to the segment set, and jump to the second step to traverse the pose points subsequent to the other pose point; If it is not a U-shaped trajectory segment, jump to the second step to traverse the pose points subsequent to the current pose point; until the traversal ends; Fourth step: Calculate the global navigation satellite system (GNSS) signal mean value of each pose trajectory segment in the segment set, and sort all the pose trajectory segments in descending order according to the mean value, and select a preset number of the pose trajectory segments with higher rankings as the extraction result of the fixed-length U-shaped trajectory segment set.

12. The device according to claim 11, wherein The processor determines whether the pose trajectory segment is a U-shaped trajectory segment, including: Determine whether the pose trajectory segment satisfies three-dimensional conditions, and when the three-dimensional conditions are satisfied, determine that the pose trajectory segment is a U-shaped trajectory segment; Among them, the three-dimensional conditions include: The difference between the yaw angles corresponding to the current pose point and the other pose point is within a preset angle range; The line segment connecting the current pose point as the starting point and the other pose point as the ending point is approximately perpendicular to the line segment connecting the current pose point as the starting point and the next adjacent pose point of the current pose point as the ending point; The straight-line distance between the current pose point and the other pose point is less than a preset distance threshold.

13. The device according to claim 12, characterized in that, The preset angle range is from 170 degrees to 190 degrees.

14. The device according to claim 12, characterized in that, The value range of the preset distance threshold is from 1 meter to 8 meters.

15. The device according to claim 11, characterized in that, The cumulative driving distances of the current pose point and the other pose point to the starting point satisfy a preset relationship, including: Add the preset trajectory segment length to the cumulative driving distance of the current pose point to the starting point to obtain a first distance, take the cumulative driving distance of the other pose point to the starting point as the second distance, and take the cumulative driving distance of the next adjacent pose point of the other pose point to the starting point as the third distance, the second distance is less than the first distance, and the first distance is less than the third distance.

16. The device according to claim 10, characterized in that, The global navigation satellite system (GNSS) signal satisfies preset conditions, including: The signal strength of the global navigation satellite system (GNSS) signal is greater than or equal to a preset signal strength threshold.

17. A system for autonomous driving, characterized in that, The system includes a positioning subsystem, a perception subsystem, a decision-making subsystem, and a control subsystem, where: The positioning subsystem is used to obtain the pose information of the autonomous vehicle in real time and transmit it to the decision-making subsystem; The perception subsystem is used to detect lanes and obstacles and transmit the detection results to the decision-making subsystem; The decision-making subsystem is used to make decisions on the autonomous vehicle by combining the data information transmitted by the positioning subsystem and the perception subsystem, and transmit the decision-making information to the control subsystem; The control subsystem is used to control the autonomous vehicle based on the decision-making information transmitted by the decision-making subsystem; Wherein, the positioning subsystem includes the calibration device of the lidar and the integrated inertial navigation according to claims 9-16, so as to perform the calibration of the lidar and the integrated inertial navigation.

18. A storage medium, characterized in that, The computer program run by the processor is stored on the storage medium, and when the computer program is run by the processor, the processor is caused to execute the calibration method of the lidar and the integrated inertial navigation according to any one of claims 1-8.

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