Lidar and combined navigation external parameter calibration method and device and intelligent vehicle

Abstract

The application relates to a laser radar and combined navigation external parameter calibration method and device and an intelligent vehicle. The application comprises the following steps: acquiring original point cloud data collected by a laser radar and original pose data collected by the combined navigation; calculating laser radar pose data corresponding to the original point cloud data based on the original point cloud data; taking the time stamp of the laser radar as a reference time, synchronizing the original pose data of the combined navigation according to the reference time to obtain synchronized pose data; calculating an initial rotation external parameter according to the laser radar pose data and the synchronized pose data of the combined navigation; and optimizing the initial rotation external parameter according to a hand-eye calibration method to obtain an optimized target external parameter. The application solves the problems of long time consumption, low result accuracy and difficult convergence of the rotation external parameter in the existing external parameter calibration method. The offline data is used for calibration, so that the calculation process is more efficient. The initial rotation external parameter is calculated, the initial rotation external parameter is taken as an initial value of the hand-eye calibration method, and an accurate external parameter value is obtained through optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent driving, and in particular to a laser radar and combined navigation external parameter calibration method and device and an intelligent vehicle. BACKGROUND

[0002] At present, various styles of sensor technology are rapidly developing. These sensors are widely used in robots, autonomous driving and other fields. Through the fusion positioning of multiple positioning sensors, more reliable positioning information with higher robustness can be obtained.

[0003] The combined navigation device and the laser radar are commonly used devices in robot high-precision positioning and perception technology. The laser radar emits a detection signal, i.e., a laser beam, and then receives a signal reflected from a target, i.e., a target echo. After comparison with the emitted signal and appropriate processing, the laser radar can obtain target-related information, such as target position, height, speed, etc. The combined navigation system is composed of a global navigation satellite system (GNSS) and an inertial navigation system (INS), and is one of the most concerned hotspots and development directions in the navigation technology field.

[0004] At present, there are two main methods for external parameter calibration of the combined navigation device and the laser radar. The first method directly measures the external parameters using a measurement sensor. However, the directions of the combined navigation device and the laser radar are different, and the direct measurement process is complex and has large measurement errors. The second method uses the registration algorithm of the laser radar to obtain the odometer information of the laser radar. The odometer information of the combined navigation is obtained, and the relative transformation matrix of the two odometers is calculated to obtain the external parameter calibration result of the two sensors. However, this method has the problems of large amount of calculation, long time consumption, low accuracy of the external parameter calibration result, and difficulty in converging the rotation external parameter in the calibration process. SUMMARY

[0005] The technical problem solved by the embodiments of the present application is to provide a laser radar and combined navigation external parameter calibration method, device and intelligent vehicle, so as to solve the problems of complex operation, long time consumption, low result accuracy and difficulty in converging the rotation external parameter in the existing external parameter calibration method, and to realize an efficient and high-precision external parameter calibration method.

[0006] In a first aspect, the embodiments of the present application provide a laser radar and combined navigation external parameter calibration method, comprising:

[0007] obtaining original point cloud data collected by the laser radar and original pose data collected by the combined navigation;

[0008] calculating laser radar pose data corresponding to the original point cloud data based on the original point cloud data;

[0009] Synchronize original pose data of the integrated navigation according to the reference time to obtain synchronized pose data of the integrated navigation, based on the reference time of the laser radar;

[0010] Calculate an initial rotation external parameter according to the laser radar pose data and the synchronized pose data of the integrated navigation;

[0011] Optimize the initial rotation external parameter according to the hand-eye calibration method to obtain an optimized target external parameter.

[0012] In some embodiments, the step of synchronizing original pose data of the integrated navigation according to the reference time to obtain synchronized pose data of the integrated navigation comprises:

[0013] Determine two time stamps adjacent to the reference time from time stamps of the integrated navigation;

[0014] Linearly interpolate original pose data of the integrated navigation according to the reference time and a difference between the two adjacent time stamps of the integrated navigation to obtain synchronized pose data of the integrated navigation.

[0015] In some embodiments, the step of calculating laser radar pose data corresponding to the original point cloud data based on the original point cloud data comprises:

[0016] Register point cloud data of adjacent frames by a normal distribution transform (DNT) algorithm;

[0017] Obtain laser radar pose data corresponding to the original point cloud data according to a pose transformation relationship obtained by registration.

[0018] In some embodiments, the integrated navigation comprises a global navigation satellite system (GNSS) and an inertial measurement unit (IMU), and the synchronized pose data of the integrated navigation comprises:

[0019] Position information synchronized between the laser radar and GNSS data;

[0020] Direction information synchronized between the laser radar and IMU data.

[0021] In some embodiments, the step of calculating an initial rotation external parameter according to the laser radar pose data and the synchronized pose data of the integrated navigation comprises:

[0022] Obtain a rotation matrix of the IMU between adjacent key frames and a rotation matrix of the laser radar between adjacent key frames;

[0023] Set an initial rotation external parameter between the laser radar and the IMU;

[0024] Establish a calibration equation according to the rotation matrix of the IMU, the rotation matrix of the laser radar and the initial rotation external parameter to obtain the initial rotation external parameter.

[0025] In some embodiments, the optimizing the initial rotation extrinsic parameter according to the hand-eye calibration method comprises:

[0026] obtaining a transformation matrix of GNSS between adjacent key frames and a transformation matrix of the lidar between adjacent key frames;

[0027] setting a translation extrinsic parameter of the lidar and the GNSS and a rotation extrinsic parameter of the lidar and the IMU;

[0028] combining the transformation matrix of the GNSS and the transformation matrix of the lidar, and optimizing the translation extrinsic parameter and the rotation extrinsic parameter by a hand-eye calibration method to obtain an optimized target extrinsic parameter.

[0029] In some embodiments, the optimizing the initial rotation extrinsic parameter according to the hand-eye calibration method further comprises:

[0030] taking the initial rotation extrinsic parameter as an initial value of a rotation extrinsic parameter in the hand-eye calibration method;

[0031] taking the assigned translation extrinsic parameter as an initial value of a translation extrinsic parameter in the hand-eye calibration method.

[0032] In a second aspect, an embodiment of the present application provides a device for calibrating extrinsic parameters of a lidar and a combined navigation, comprising:

[0033] an acquisition module configured to acquire original point cloud data collected by the lidar and original pose data collected by the combined navigation;

[0034] a first calculation module configured to calculate lidar pose data corresponding to the original point cloud data based on the original point cloud data;

[0035] a synchronization module configured to take a timestamp of the lidar as a reference time, and synchronize the original pose data of the combined navigation according to the reference time to obtain synchronized pose data;

[0036] a second calculation module configured to calculate an initial rotation extrinsic parameter according to the lidar pose data and the synchronized pose data of the combined navigation;

[0037] an optimization module configured to optimize the initial rotation extrinsic parameter according to a hand-eye calibration method to obtain an optimized target extrinsic parameter.

[0038] In a third aspect, an embodiment of the present application provides an intelligent vehicle, comprising: at least one processor; and a memory communicatively connected to the at least one processor;

[0039] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method for calibrating extrinsic parameters of a laser radar and a combined navigation according to the first aspect.

[0040] In a fourth aspect, the embodiments of the present application provide a nonvolatile computer-readable storage medium storing computer executable instructions, when the computer executable instructions are executed by an intelligent vehicle, the intelligent vehicle executes the method for calibrating extrinsic parameters of a laser radar and a combined navigation according to the first aspect.

[0041] The embodiments of the present application have the following beneficial effects: Different from the related art, the method, device and intelligent vehicle for calibrating extrinsic parameters of a laser radar and a combined navigation provided by the embodiments of the present application, by acquiring original point cloud data collected by the laser radar and original pose data collected by the combined navigation, calculating laser radar pose data corresponding to the original point cloud data based on the original point cloud data, taking a timestamp of the laser radar as a reference time, synchronizing the original pose data of the combined navigation according to the reference time to obtain synchronized pose data, calculating an initial rotation extrinsic parameter according to the laser radar pose data and the synchronized pose data of the combined navigation, and optimizing the initial rotation extrinsic parameter according to a hand-eye calibration method to obtain an optimized target extrinsic parameter, the embodiments of the present application solve the problems of complex operation, long time consumption, low accuracy of results and difficulty in converging the rotation extrinsic parameter in the existing extrinsic parameter calibration method, and the use of offline data for calibration makes the calculation process more efficient, and the initial rotation extrinsic parameter is calculated and used as an initial value of the hand-eye calibration method to optimize the extrinsic parameter value. BRIEF DESCRIPTION OF DRAWINGS

[0042] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, which are schematic and not intended to be limiting of the embodiments, and in which like numerals refer to like elements throughout the drawings. The drawings are not limiting in scope since aspects of variations can be implemented in any appropriate types of environments.

[0043] Figure 1 is a flowchart of a method for calibrating extrinsic parameters of a laser radar and a combined navigation provided by the embodiments of the present application;

[0044] Figure 2 is a structural diagram of a device for calibrating extrinsic parameters of a laser radar and a combined navigation provided by the embodiments of the present application;

[0045] Figure 3 is a structural diagram of an intelligent vehicle provided by the embodiments of the present application. DETAILED DESCRIPTION

[0046] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application and not to limit the present application.

[0047] It should be noted that the various features of the embodiments of the present application can be combined with each other if there is no conflict, and are within the protection scope of the present application. In addition, although the functional modules are divided in the device schematic diagram, and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the device schematic diagram or the order in the flowchart.

[0048] Unless otherwise defined, all technical and scientific terms used in the specification are the same as those commonly understood by those skilled in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments and are not intended to limit the present application.

[0049] In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as there is no conflict.

[0050] Please refer to Figure 1 , Figure 1 is a flowchart of a method for calibrating external parameters of a laser radar and a combined navigation provided by an embodiment of the present application, and the method comprises the following steps:

[0051] Step S1: Obtain original point cloud data collected by the laser radar and original pose data collected by the combined navigation.

[0052] The combined navigation in the embodiments of the present application is a combined navigation including GNSS and IMU. GNSS (Global Navigation Satellite System) is an air-based radio navigation positioning system that can provide three-dimensional coordinates, speed and time information for users at any location on the earth's surface or near space at all times, including one or more satellite constellations and the enhanced system required to support a particular work.

[0053] IMU (Inertial Measurement Unit) is a device that measures the three-axis attitude angle and acceleration of an object, and can calculate the motion attitude of the object in a period of time.

[0054] The laser radar is a radar system that detects the position, speed and other characteristic quantities of a target by emitting a laser beam. In the embodiments of the present application, the laser radar is mainly a 3D laser radar, which can obtain three-dimensional depth information and point cloud data, and has the advantages of high resolution and low power consumption.

[0055] The combined navigation of the GNSS and the IMU can obtain satellite navigation data and inertial measurement data at the same time, thereby providing data support for the external parameter calibration process.

[0056] It should be noted that, in order to freely select high-quality sensors and ensure the effectiveness of data, the start time and the end time of data set interception can be set for the sensors in the application. In order to have sufficient data support, the data set is greater than 40 frames. Whether the current intercepted data of the sensor odometer meets the calibration condition is checked through the rviz three-dimensional visualization platform, wherein the calibration condition is that the intelligent vehicle has a sufficient rotation angle, and the rotation angle is greater than 45 degrees. When recording data, the intelligent vehicle can adopt an 8-shaped route or an S-shaped route for driving.

[0057] It should be noted that, in order to save the time consumption of the calculation unit, the external calibration of the offline data is adopted in the embodiment of the application. The data of the laser radar and the combined navigation are recorded in advance through the ROS (The Robot Operating System, robot operating system), and the external parameters are calibrated offline through an algorithm. The external parameters are input to the intelligent vehicle positioning framework in the YAML file format.

[0058] Step S2: calculating laser radar pose data corresponding to the original point cloud data based on the original point cloud data.

[0059] In some embodiments, the calculation of the laser radar pose data corresponding to the original point cloud data based on the original point cloud data comprises: registering the point cloud data of adjacent frames through a normal distribution DNT algorithm, and obtaining the laser radar pose data corresponding to the original point cloud data according to the pose transformation relationship obtained through the registration.

[0060] The original point cloud data collected by the laser radar is registered through the use of the NDT algorithm on adjacent frames of the original point cloud data, and the laser radar pose data is obtained according to the pose transformation integral obtained through the registration.

[0061] NDT (Normal Distributed Transform, normal distribution transform algorithm) is a point cloud registration algorithm, which is applied to a statistical model of three-dimensional point cloud, and uses an iterative optimization method to determine the optimal matching between two point clouds and obtain the corresponding pose transformation.

[0062] Step S3: taking the timestamp of the laser radar as a reference time, synchronizing the original pose data of the combined navigation according to the reference time, and obtaining synchronized pose data.

[0063] Since the data acquisition frequency of the laser radar and the combined navigation is usually different, the control device cannot simultaneously acquire the original point cloud data of the laser radar and the original pose data of the combined navigation, and therefore, it is necessary to align the laser radar pose data with the original pose data of the combined navigation based on the reference time to obtain the aligned and synchronized pose data.

[0064] In some embodiments, the step of synchronizing the original pose data of the combined navigation according to the reference time to obtain the synchronized pose data comprises the following steps: determining two time stamps adjacent to the reference time from the time stamps of the combined navigation, performing linear difference on the original pose data of the combined navigation according to the reference time and the difference between the two determined adjacent time stamps of the combined navigation to obtain the synchronized pose data of the combined navigation.

[0065] For example, the laser radar pose data of the reference time t1 is M, there are two time stamps t0 and t2 adjacent to t1 in the combined navigation, the original pose data of the combined navigation corresponding to the time stamp t0 is X1, and the pose data of the combined navigation corresponding to the time stamp t2 is X2, the pose data X of the combined navigation corresponding to the reference time t1 is estimated by using the original pose data X1 of the combined navigation corresponding to the time stamp t0 and the pose data X2 of the combined navigation corresponding to the time stamp t2, wherein t0 < t1 < t2, and the following can be obtained by linear difference method:

[0066]

[0067] In some embodiments, the combined navigation comprises GNSS and IMU, and the synchronized pose data of the combined navigation comprises position information synchronized with the laser radar and GNSS data and direction information synchronized with the laser radar and IMU data.

[0068] By finding the nearest adjacent pose data of the combined navigation at the reference time and performing linear interpolation through the time difference, the same odometer frequency as the laser radar is obtained, thereby solving the problem of misalignment of time stamps between different sensors.

[0069] Step S4: calculating an initial rotation external parameter according to the laser radar pose data and the synchronized pose data of the combined navigation.

[0070] In the embodiments of the present application, in order to ensure faster convergence of the external parameter and obtain a more accurate external parameter, an initial rotation external parameter needs to be calculated according to the laser radar pose data and the synchronized pose data of the combined navigation.

[0071] In some embodiments, the calculating the initial rotation extrinsic parameter according to the lidar pose data and the synchronous pose data of the integrated navigation comprises: obtaining a rotation matrix of the IMU between adjacent key frames and a rotation matrix of the lidar between the adjacent key frames, setting an initial rotation extrinsic parameter between the lidar and the IMU, and establishing a calibration equation according to the rotation matrix of the IMU, the rotation matrix of the lidar and the initial rotation extrinsic parameter to obtain the initial rotation extrinsic parameter.

[0072] wherein the calculation formula of the rotation extrinsic parameter is:

[0073]

[0074] wherein, represents the quaternion of the k+1th frame of IMU data to the kth frame;

[0075] represents the extrinsic parameter of the lidar to the IMU;

[0076] represents the data of the k+1th frame of lidar data to the kth frame.

[0077] By deforming and superimposing a plurality of measurement values at different times, a calibration equation is obtained: according to It can be derived that:

[0078]

[0079] superimposed:

[0080]

[0081] wherein Q N represents represents the extrinsic parameter of the lidar to the IMU, [q] L represents the left quaternion product matrix, [q] R represents the right quaternion product matrix, α k is the weight of each rotation pair, which is determined in a heuristic manner to suppress outliers.

[0082] It should be noted that the extrinsic parameter of the lidar to the IMU is the initial rotation extrinsic parameter.

[0083] Step S5: optimizing the initial rotation extrinsic parameter according to the hand-eye calibration method to obtain an optimized target extrinsic parameter.

[0084] The hand-eye calibration is the earliest method for calibrating the rigid body coordinate conversion between the camera at the end of the robot and the end of the robot. In the world coordinate system, for any motion at any time, the robot end pose A and the camera pose B satisfy the motion constraint AH=HB, and the extrinsic parameter H can be obtained by optimizing the solution of the target equation.

[0085] In some embodiments, the initial rotation extrinsic parameter is optimized according to a hand-eye calibration method, comprising: obtaining a transformation matrix of GNSS between adjacent key frames, and a transformation matrix of the lidar between adjacent key frames, setting a translation extrinsic parameter of the lidar and GNSS and a rotation extrinsic parameter of the lidar and the IMU, combining the transformation matrix of the GNSS and the transformation matrix of the lidar, and optimizing the translation extrinsic parameter and the rotation extrinsic parameter by the hand-eye calibration method to obtain an optimized target extrinsic parameter.

[0086] wherein the calculation formula of the hand-eye calibration is:

[0087]

[0088] wherein T represents a transformation matrix R represents a rotation matrix, and t represents a translation matrix.

[0089] represents a transformation matrix of the GNSS odometry of the k+1 frame to the k frame.

[0090] represents a translation matrix of the lidar to the GNSS, and a rotation matrix of the lidar to the IMU.

[0091] represents a transformation matrix of the lidar data of the k+1 frame to the k frame.

[0092] In some embodiments, the initial rotation extrinsic parameter is optimized according to the hand-eye calibration method, further comprising: taking the initial rotation extrinsic parameter as an initial value of the rotation extrinsic parameter in the hand-eye calibration method; and taking the assigned translation extrinsic parameter as an initial value of the translation extrinsic parameter in the hand-eye calibration method.

[0093] Taking the initial rotation extrinsic parameter as the initial value of the rotation extrinsic parameter in the hand-eye calibration method can be understood that the initial value of the rotation matrix R is given by Taking the assigned translation extrinsic parameter as the initial value of the translation extrinsic parameter in the hand-eye calibration method, for example, directly assigning [0, 0, 0] as the initial value of the translation matrix.

[0094] In summary, the laser radar and combined navigation external parameter calibration method, device and intelligent vehicle provided by the embodiments of the present application obtain the original point cloud data collected by the laser radar and the original pose data collected by the combined navigation, calculate the laser radar pose data corresponding to the original point cloud data based on the original point cloud data, take the timestamp of the laser radar as the reference time, synchronize the original pose data of the combined navigation according to the reference time to obtain synchronized pose data, calculate the initial rotation external parameter according to the laser radar pose data and the synchronized pose data of the combined navigation, and optimize the initial rotation external parameter according to the hand-eye calibration method to obtain the optimized target external parameter. The present application solves the problems of complex operation, long time consumption, low result accuracy and difficult convergence of the rotation external parameter in the existing external parameter calibration method, and makes the calculation process more efficient by calibrating offline data. The initial rotation external parameter is calculated, the initial rotation external parameter is taken as the initial value of the hand-eye calibration method, and the accurate external parameter value is obtained through optimization.

[0095] Please refer to Figure 2 , Figure 2 is a structural schematic diagram of a laser radar and combined navigation external parameter calibration device provided by the embodiments of the present application. The device 100 comprises an acquisition module 101, a first calculation module 102, a synchronization module 103, a second calculation module 104 and an optimization module 105.

[0096] The acquisition module 101 is configured to acquire original point cloud data collected by the laser radar and original pose data collected by the combined navigation. The first calculation module 102 is configured to calculate laser radar pose data corresponding to the original point cloud data based on the original point cloud data. The synchronization module 103 is configured to take the timestamp of the laser radar as the reference time, synchronize the original pose data of the combined navigation according to the reference time, and obtain synchronized pose data. The second calculation module 104 is configured to calculate an initial rotation external parameter according to the laser radar pose data and the synchronized pose data of the combined navigation. The optimization module 105 is configured to optimize the initial rotation external parameter according to the hand-eye calibration method to obtain an optimized target external parameter.

[0097] Specifically, the synchronization module 101 comprises a first determination unit and a second determination unit. The first determination unit is configured to determine two time stamps adjacent to the reference time from the time stamps of the combined navigation. The second determination unit is configured to linearly difference the original pose data of the combined navigation according to the difference between the reference time and the two adjacent combined navigation time stamps to obtain the synchronized pose data of the combined navigation.

[0098] The calculation module 102 comprises a registration unit and a processing unit. The registration unit is configured to register the point cloud data of adjacent frames by the normal distribution transform (DNT) algorithm. The processing unit is configured to obtain the laser radar pose data corresponding to the original point cloud data according to the pose transformation relationship obtained by registration.

[0099] The synchronization pose data of the combined navigation including GNSS and IMU comprises: position information of the lidar synchronized with GNSS data, and direction information of the lidar synchronized with IMU data.

[0100] The second computing module 104 comprises a first acquisition unit, a first setting unit and a establishing unit. The first acquisition unit is configured to acquire the rotation matrix of the IMU between adjacent key frames and the rotation matrix of the lidar between adjacent key frames. The first setting unit is configured to set the initial rotation extrinsic parameter between the lidar and the IMU. The establishing unit is configured to establish a calibration equation according to the rotation matrix of the IMU, the rotation matrix of the lidar and the initial rotation extrinsic parameter to obtain the initial rotation extrinsic parameter.

[0101] The optimization module 105 comprises a second acquisition unit, a second setting unit and an optimization unit. The second acquisition unit is configured to acquire the transformation matrix of the GNSS between adjacent key frames and the transformation matrix of the lidar between adjacent key frames. The second setting unit is configured to set the translation extrinsic parameter of the lidar and the GNSS and the rotation extrinsic parameter of the lidar and the IMU. The optimization unit is configured to optimize the translation extrinsic parameter and the rotation extrinsic parameter by the hand-eye calibration method in combination with the transformation matrix of the GNSS and the transformation matrix of the lidar to obtain the optimized target extrinsic parameter.

[0102] According to the hand-eye calibration method to optimize the initial rotation extrinsic parameter, the initial rotation extrinsic parameter is taken as the initial value of the rotation extrinsic parameter in the hand-eye calibration method, and the assigned translation extrinsic parameter is taken as the initial value of the translation extrinsic parameter in the hand-eye calibration method.

[0103] In the embodiments of the present application, the extrinsic parameter calibration device of the lidar and the combined navigation can also be built by hardware devices, for example, the extrinsic parameter calibration of the lidar and the combined navigation can be built by one or more than two chips, and each chip can work in coordination with each other to complete the application of the extrinsic parameter calibration method of the lidar and the combined navigation described in each embodiment. For another example, the extrinsic parameter calibration device of the lidar and the combined navigation can also be built by various logic devices, such as general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), single-chip microcomputers, ARM (Acorn RISC Machine) or other programmable logic devices, discrete gates or transistor logic, discrete hardware components or any combination of these components.

[0104] The laser radar and combined navigation external parameter calibration device in the embodiments of the present application can be a device with an operating system. The operating system can be an Android operating system, an ios operating system, or other possible operating systems, and the embodiments of the present application do not make specific limitations.

[0105] It should be noted that the information interaction, execution process and the like between various modules and units in the laser radar and combined navigation external parameter calibration device in the embodiments of the present application are based on the same concept as the method embodiments of the present application, and the specific content is also applicable to the laser radar and combined navigation external parameter calibration device. The various modules in the embodiments of the present application can be realized as separate hardware or software, and the functions of the various units can be realized in combination according to the needs using separate hardware or software.

[0106] The present application also provides an intelligent vehicle, please refer to Figure 3 , Figure 3 is a structural schematic diagram of an intelligent vehicle provided by the embodiments of the present application. The intelligent vehicle 200 includes at least one processor 201, and a memory 202 in communication connection with the at least one processor 201, wherein the memory 202 stores instructions executable by the at least one processor 201, and the instructions are executed by the at least one processor 201 to enable the at least one processor 201 to execute the laser radar and combined navigation external parameter calibration method in any of the above method embodiments. The processor 201 and the memory 202 can be connected by a bus or other means, Figure 3 in the present application take the bus connection as an example.

[0107] The processor 201 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a hardware chip or any combination thereof; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The above-mentioned PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof.

[0108] The memory 202, as a non-transitory computer readable storage medium, can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the visual positioning method in the embodiments of the present application. The processor 201 can implement the laser radar and combined navigation external parameter calibration method in any of the above method embodiments by running the non-transitory software programs, instructions and modules stored in the memory 202, that is, can implement the entire process of Figure 1 .

[0109] The embodiments of the present application provide a non-volatile computer readable storage medium, which stores computer executable instructions, for example, a memory including program codes, the above-mentioned program codes can be executed by a processor to complete the laser radar and combined navigation external parameter calibration method in the above-mentioned embodiments. For example, the computer readable storage medium can be a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a read-only compact disc (Compact Disc Read-Only Memory, CDROM), a magnetic tape, a floppy disk and an optical data storage device, etc.

[0110] The embodiments of the present application provide a computer program product, which includes one or more program codes stored in a computer readable storage medium. The processor of the intelligent vehicle reads the program codes from the computer readable storage medium, and the processor executes the program codes to complete the steps of the laser radar and combined navigation external parameter calibration method provided in the above-mentioned embodiments.

[0111] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; under the idea of the present application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes of different aspects of the present application as described above, which are not provided in details for simplicity; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for calibrating extrinsic parameters of a laser radar and a combined navigation, characterized in that, The method comprises the following steps: acquiring raw point cloud data collected by the laser radar and raw pose data collected by the integrated navigation; calculating laser radar pose data corresponding to the raw point cloud data based on the raw point cloud data; determining two time stamps adjacent to a reference time stamp of the laser radar from time stamps of the integrated navigation; linearly interpolating the raw pose data of the integrated navigation according to a difference between the reference time stamp and the two adjacent time stamps of the integrated navigation to obtain synchronized pose data of the integrated navigation; acquiring a rotation matrix of the IMU between adjacent key frames and a rotation matrix of the laser radar between adjacent key frames, setting an initial rotation external parameter between the laser radar and the IMU, and establishing a calibration equation according to the rotation matrix of the IMU, the rotation matrix of the laser radar and the initial rotation external parameter to obtain the initial rotation external parameter; optimizing the initial rotation external parameter according to a hand-eye calibration method to obtain an optimized target external parameter. The integrated navigation comprises a GNSS and an IMU, and the synchronized pose data of the integrated navigation comprises position information synchronized with the laser radar and GNSS data and direction information synchronized with the laser radar and IMU data.

2. The method of claim 1, wherein, The method of calculating the laser radar pose data corresponding to the raw point cloud data based on the raw point cloud data comprises the following steps: registering point cloud data of adjacent frames by a normal distribution transform (DNT) algorithm; obtaining the laser radar pose data corresponding to the raw point cloud data according to a pose transformation relationship obtained by registration.

3. The method of claim 1, wherein, The method of optimizing the initial rotation external parameter according to the hand-eye calibration method comprises the following steps: acquiring a transformation matrix of the GNSS between adjacent key frames and a transformation matrix of the laser radar between adjacent key frames; setting a translation external parameter of the laser radar and the GNSS and a rotation external parameter of the laser radar and the IMU; optimizing the translation external parameter and the rotation external parameter by a hand-eye calibration method in combination with the transformation matrix of the GNSS and the transformation matrix of the laser radar to obtain the optimized target external parameter.

4. The method of claim 3, wherein, The method of optimizing the initial rotation external parameter according to the hand-eye calibration method further comprises the following steps: taking the initial rotation external parameter as an initial value of a rotation external parameter in the hand-eye calibration method; taking the assigned translation external parameter as an initial value of a translation external parameter in the hand-eye calibration method.

5. An apparatus for calibrating the external parameters of a laser radar and a combined navigation system, characterized in that, The method comprises the following steps: an acquisition module configured to acquire raw point cloud data collected by the laser radar and raw pose data collected by the integrated navigation; a first calculation module configured to calculate laser radar pose data corresponding to the raw point cloud data based on the raw point cloud data; a synchronization module configured to synchronize raw pose data of the integrated navigation according to a reference time stamp of the laser radar to obtain synchronized pose data; a second calculation module configured to calculate an initial rotation external parameter according to the laser radar pose data and the synchronized pose data of the integrated navigation; an optimization module configured to optimize the initial rotation external parameter according to a hand-eye calibration method to obtain an optimized target external parameter.

6. An intelligent vehicle, characterized by The method comprises the following steps: at least one processor; and a memory connected in communication with the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method for calibrating the external parameters of the laser radar and the integrated navigation according to any one of claims 1 to 4.

7. A non-transitory computer readable storage medium, comprising: The non-volatile computer readable storage medium stores computer executable instructions, and when the computer executable instructions are executed by the intelligent vehicle, the intelligent vehicle performs the method for calibrating the external parameters of the laser radar and the integrated navigation according to any one of claims 1 to 4.

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