Global point cloud map real-time construction method and device, electronic equipment and storage medium

Abstract

The invention discloses a global point cloud map real-time construction method, which comprises the following steps: acquiring laser point cloud data and inertial measurement data of a current sampling frame of a target scene, and performing distortion correction on the laser point cloud data of the current sampling frame according to state estimation data of a previous sampling frame and the inertial measurement data of the current sampling frame, the method comprises the steps of obtaining corrected point cloud data, calculating state estimation data of a current sampling frame by using an error state iterative Kalman filtering algorithm based on the corrected point cloud data, and performing coordinate transformation on the corrected point cloud data according to the state estimation data of the current sampling frame to generate a real-time global point cloud map of a target scene.

Description

Technical Field

[0001] The present invention belongs to the technology of three - dimensional map construction, and particularly relates to a method, device, electronic device and storage medium for real - time construction of a global point cloud map. Background Art

[0002] With the continuous development of lidar scanning technology and inertial measurement unit (IMU) technology, three - dimensional point cloud construction methods based on multi - sensor fusion have been widely applied in fields such as autonomous driving, robot navigation, and industrial inspection. Existing methods usually combine the measurement data of lidar and IMU, and use kinematic models and filtering algorithms to achieve high - precision modeling of the target environment. However, existing technologies still face many challenges in the application process.

[0003] Firstly, due to the coordinate system differences and time synchronization errors between lidar and IMU, point cloud data often has distortions during the sensor fusion process, which in turn affects the accuracy of the finally generated three - dimensional map. Secondly, although various algorithms are used to optimize point cloud data and system state estimation, in complex scenarios or high - dynamic environments, there are still problems in the balance between real - time performance and accuracy for existing technologies. Moreover, sensor noise, changes in the dynamic environment, and the computational complexity of algorithms often lead to the accumulation of errors during the point cloud construction process, affecting the update accuracy of the global map and the robustness of the system.

[0004] Therefore, it is urgent to further improve the accuracy of point cloud data and the estimation accuracy of the system state on the basis of existing technologies to cope with the challenges in a dynamically changing environment and achieve more reliable and efficient global map construction. Summary of the Invention

[0005] Based on this, the present invention aims to propose a method for real - time construction of a global point cloud map, which obtains scanned point clouds of the target scene based on motion state estimation, and performs real - time estimation of the system motion state through an error - state iterative Kalman filter algorithm, thereby realizing the accurate and real - time construction of the global point cloud map of the target scene.

[0006] In a first aspect, the present invention provides a method for real - time construction of a global point cloud map, including:

[0007] Obtain the lidar point cloud data and inertial measurement data of the current sampling frame of the target scene;

[0008] Perform distortion correction on the lidar point cloud data of the current sampling frame according to the state estimation data of the previous sampling frame and the inertial measurement data of the current sampling frame to obtain corrected point cloud data;

[0009] Based on the corrected point cloud data, calculate the state estimation data of the current sampling frame by using the error - state iterative Kalman filter algorithm;

[0010] Coordinate transformation is performed on the corrected point cloud data according to the state estimation data of the current sampling frame to generate a real-time global point cloud map of the target scene.

[0011] Furthermore, distortion correction is performed on the lidar point cloud data of the current sampling frame according to the state estimation data of the previous sampling frame and the inertial measurement data of the current sampling frame. The obtained corrected point cloud data includes:

[0012] Construct an inertial measurement kinematic model using the inertial measurement data of the current sampling frame;

[0013] Calculate the prior estimation data of the current sampling frame according to the inertial measurement kinematic model and the state estimation data of the previous sampling frame;

[0014] Perform coordinate transformation on the lidar point cloud data of the current sampling frame using the prior estimation data of the current sampling frame to obtain the corrected point cloud data.

[0015] Furthermore, performing coordinate transformation on the lidar point cloud data of the current sampling frame using the prior estimation data of the current sampling frame to obtain the corrected point cloud data includes:

[0016] Transform the lidar point cloud data of the current sampling frame from the lidar coordinate system to the IMU coordinate system to obtain the first point cloud data; use the prior estimation data of the current sampling frame to transform the first point cloud data from the IMU coordinate system to the IMU coordinate system at the end time of the current sampling frame to obtain the second point cloud data; transform the second point cloud data from the IMU coordinate system at the end time of the current sampling frame to the lidar coordinate system to obtain the corrected point cloud data.

[0017] Furthermore, the state estimation data of the current sampling frame includes the system state and the error state. Using the error state iterative Kalman filter algorithm to calculate the state estimation data of the current sampling frame includes:

[0018] Calculate the observation residual of the current iteration round according to the corrected point cloud data and the observation model;

[0019] Calculate the Kalman gain, and calculate the error state of the current iteration round according to the Kalman gain and the observation residual;

[0020] Calculate the system state of the current iteration round according to the error state of the current iteration round and the prior estimation data of the current sampling frame;

[0021] When the system state of the current iteration round meets the set convergence condition, output the optimal value of the system state.

[0022] Furthermore, the observation residual is calculated as follows:

[0023]

[0024] Where denotes the observation residual of the k-th sampling frame at the i-th iteration round, denotes the Jacobian matrix, calculated based on the calibrated point cloud data, denotes the prior estimate of the error state of the k-th sampling frame at the (i - 1)-th iteration round, denotes the observation noise, , denotes the observation noise of the covariance matrix.

[0025] Further, the convergence condition is set to include that the change in the system state of the current sampling frame relative to the system state of the previous sampling frame is less than a set threshold, or the number of iterations reaches a set value.

[0026] Further, the coordinate transformation of the calibrated point cloud data according to the state estimation data of the current sampling frame includes:

[0027] Constructing a pose transformation matrix according to the state estimation data of the current sampling frame;

[0028] Using the pose transformation matrix to transform the calibrated point cloud data from the lidar coordinate system to the global coordinate system, where the global coordinate system is the coordinate system where the IMU frame is located at the initial moment of the system.

[0029] In a second aspect, the present invention provides a device for real-time construction of a global point cloud map, including:

[0030] A data acquisition module, configured to acquire the lidar point cloud data and inertial measurement data of the current sampling frame of the target scene;

[0031] A point cloud calibration module, configured to perform distortion calibration on the lidar point cloud data of the current sampling frame according to the state estimation data of the previous sampling frame and the inertial measurement data of the current sampling frame to obtain calibrated point cloud data;

[0032] A state estimation module, configured to calculate the state estimation data of the current sampling frame based on the calibrated point cloud data by using the error state iterative Kalman filtering algorithm;

[0033] A point cloud map generation module, configured to perform coordinate transformation on the calibrated point cloud data according to the state estimation data of the current sampling frame to generate a real-time global point cloud map of the target scene.

[0034] In a third aspect, the present invention provides an electronic device, including a memory storing computer-executable instructions and a processor, and when the computer-executable instructions are executed by the processor, the device executes each step of the method for real-time construction of a global point cloud map provided in the first aspect.

[0035] Fourthly, the present invention provides a readable storage medium storing a computer-executable program, which can implement the steps of the real-time global point cloud map construction method provided in the first aspect when the program is executed.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The present invention proposes a real-time global point cloud map construction method, including obtaining the laser point cloud data and inertial measurement data of the current sampling frame of the target scene, correcting the distortion of the laser point cloud data of the current sampling frame according to the state estimation data of the previous sampling frame and the inertial measurement data of the current sampling frame to obtain corrected point cloud data, calculating the state estimation data of the current sampling frame based on the corrected point cloud data by using the error state iterative Kalman filter algorithm, and performing coordinate transformation on the corrected point cloud data according to the state estimation data of the current sampling frame to generate a real-time global point cloud map of the target scene; the method proposed by the present invention uses inertial measurement data to perform prior prediction on the motion state of the sampling frame and corrects the point cloud distortion, so as to iteratively correct the state prediction value by using the error state iterative Kalman filter algorithm in combination with the laser point cloud data to make it approach the true value of the system, and finally update the sampling frame point cloud data to the global point cloud map to achieve the real-time and accurate construction of the global point cloud map of the target scene. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0039] Figure 1 is the implementation flowchart of the real-time global point cloud map construction method provided by the embodiment of the present invention;

[0040] Figure 2 is the structural diagram of the real-time global point cloud map construction device provided by the embodiment of the present invention;

[0041] Figure 3 is the architecture diagram of the electronic device provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0043] Refer to Figure 1 , an embodiment of the present invention provides a method for real-time construction of a global point cloud map, including the following steps:

[0044] Step S110. Obtain the lidar point cloud data and inertial measurement data of the current sampling frame of the target scene.

[0045] In this step, a sampling frame refers to the lidar continuously scanning the target scene at a certain frequency, and a set of point cloud data is generated in each scanning cycle. Such a scanning cycle is called a sampling frame. Therefore, a sampling frame includes a scanning start time and a scanning end time. The lidar point cloud data can be obtained by the lidar emitting laser beams, measuring the return time, and calculating the three-dimensional coordinates (X, Y, Z) of each laser point to form point cloud data. The inertial measurement data is the three-axis angular velocity and three-axis acceleration information provided by the sensors of the inertial measurement unit (IMU). Among them, the three-axis angular velocity is measured by the gyroscope in the IMU, and the three-axis acceleration is measured by the accelerometer in the IMU, which is used to deduce the motion state of the device.

[0046] Exemplarily, the inertial measurement data can be expressed as follows:

[0047]

[0048] Among them, represents the three-axis acceleration, represents the three-axis angular velocity, represents the sampling frame number.

[0049] Step S120. Perform distortion correction on the lidar point cloud data of the current sampling frame according to the state estimation data of the previous sampling frame and the inertial measurement data of the current sampling frame to obtain corrected point cloud data.

[0050] Specifically, step S120 includes the following steps:

[0051] Step S121. Construct an inertial measurement kinematic model using the inertial measurement data of the current sampling frame.

[0052] In this step, the position, velocity, and attitude of the object are deduced from the measured three-axis acceleration and three-axis angular velocity. Specifically, the acceleration of the object in the world coordinate system is calculated according to the output of the accelerometer and the rotation matrix, the attitude of the object is obtained by integrating according to the angular velocity output of the gyroscope, and finally the velocity and position of the object are calculated by numerical integration.

[0053] Step S122. Calculate the prior estimation data of the current sampling frame according to the inertial measurement kinematic model and the state estimation data of the previous sampling frame.

[0054] The prior estimate data is calculated in the current sampling frame based on the system state and control input of the previous sampling frame through the propagation of the kinematic model and process noise, reflecting the prediction of the system state and its uncertainty description before updating with the current observation.

[0055] Specifically, prediction is performed according to the kinematic model of the IMU, the system state and error state of the previous sampling frame, to obtain the prior estimate value of the nominal state of the current sampling. 、the prior estimate value of the error state and the covariance matrix . For further embodiments, the optimal system state and optimal error state of the previous sampling frame are adopted.

[0056] The prior estimate value of the nominal state refers to the predicted value of the state variables of the IMU (such as position, velocity, attitude, etc.); the error state estimate value is the error of the system state, describing the deviation between the current predicted state and the true state; the covariance matrix represents the estimated uncertainty of the error state and is usually used to describe the accuracy of the estimate.

[0057] Step S123. Use the prior estimate data of the current sampling frame to perform coordinate transformation on the lidar point cloud data of the current sampling frame to obtain corrected point cloud data.

[0058] In the acquisition of lidar point cloud data, since the lidar needs a certain time to complete a full scan, while the IMU provides motion state information at a higher frequency, different scan points are collected at different timestamps, resulting in motion distortion of the point cloud. To correct the distortion of the point cloud, in this embodiment, the prior estimate value of the nominal state in the prior estimate data is used to calculate the coordinates of the lidar points at a unified time.

[0059] Specifically, the prior estimate value of the nominal state is used to correct the distortion of the lidar point cloud to eliminate the motion deviation caused by the dynamic motion of the system. Since the prior estimate value of the nominal state includes the state variables of the IMU, it can be used to transform and correct the point cloud data.

[0060] Further, the transformation of the corrected point cloud data in step S123 includes the following process:

[0061] Transform the lidar point cloud data of the current sampling frame from the lidar coordinate system to the IMU coordinate system to obtain the first point cloud data; use the prior estimate data of the current sampling frame to transform the first point cloud data from the IMU coordinate system to the IMU coordinate system at the end time of the current sampling frame to obtain the second point cloud data; transform the second point cloud data from the IMU coordinate system at the end time of the current sampling frame to the lidar coordinate system to obtain the corrected point cloud data.

[0062] Specifically, for each laser point, it is necessary to first convert it from the lidar coordinate system to the IMU coordinate system. The transformation matrix can be obtained by calibrating the extrinsic parameters of the lidar and the IMU. Then, each laser point is converted from the IMU coordinate system to the IMU coordinate system at the end of the scan. This process is based on the state prediction result of the IMU. The nominal state prior estimate provides the IMU motion state at the scan time. Using this information, the point cloud data is converted from the scan time to the end of the scan time to obtain the IMU state at the end of the scan. Finally, the laser points in the IMU coordinate system at the end of the scan are converted back to the lidar coordinate system to obtain the laser point cloud data after distortion correction. All laser points are unified at the same time, thus completing the distortion correction.

[0063] Exemplarily, assume that the laser point cloud data obtained by laser scanning in the current sampling frame is represented as:

[0064]

[0065] where represents the number of laser points.

[0066] The process of performing distortion correction on it can be expressed as:

[0067]

[0068] where represents the extrinsic parameter matrix from the lidar coordinate system to the IMU coordinate system, represents the transformation matrix from the IMU coordinate system where the 𝑗-th laser point is located to the IMU coordinate system at the end of the scan, represents the extrinsic parameter matrix from the IMU coordinate system to the lidar coordinate system.

[0069] Step S130. Based on the corrected point cloud data, use the error-state iterative Kalman filter algorithm to calculate the state estimation data of the current sampling frame.

[0070] In this step, the prior estimation data of the current sampling frame is updated using the prior estimation data and the corrected point cloud data of the current sampling frame, that is, the prior estimation data and the current observation value are fused through observation update (delayed estimation) and calculated by the Kalman filter algorithm.

[0071] Specifically, step S130 includes the following steps:

[0072] Calculate the observation residual of the current iteration round according to the corrected point cloud data and the observation model;

[0073] Calculate the Kalman gain, and calculate the error state of the current iteration round according to the Kalman gain and the observation residual;

[0074] Calculate the system state of the current iteration round based on the error state of the current iteration round and the prior estimation data of the current sampling frame;

[0075] When the system state of the current iteration round meets the set convergence condition, output the optimal value of the system state.

[0076] In a further implementation manner, the specific calculation process of step S130 is as follows:

[0077] Based on the calibrated point cloud data and the observation model, calculate the difference between the observed value and the predicted value to obtain the observation residual of the k-th sampling frame in the i-th iteration round, that is:

[0078]

[0079] where represents the observation residual of the k-th sampling frame in the i-th iteration round, represents the Jacobian matrix, calculated based on the calibrated point cloud data, represents the prior estimated value of the error state of the k-th sampling frame in the (i - 1)-th iteration round, represents the observation noise, , represents the observation noise of the covariance matrix.

[0080] Calculate the Kalman gain as

[0081]

[0082] Calculate the error state of the current iteration round based on the Kalman gain and the observation residual:

[0083]

[0084] Superimpose and calculate the error state of the current iteration round and the prior estimation data of the current sampling frame to obtain the system state of the current iteration round:

[0085]

[0086] If the change in the system state of the current sampling frame relative to the system state of the previous sampling frame is less than the set threshold, that is , is the vector norm, representing the change amplitude of the system state; or the number of iterations reaches the set value, then it is considered that the iterative update converges, and record the optimal value of the system state of the current sampling frame as , otherwise continue the aforementioned iterative update process.

[0087] Step S140. Coordinate-transform the calibrated point cloud data according to the state estimation data of the current sampling frame to generate a real-time global point cloud map of the target scene.

[0088] Specifically, in this step, a pose transformation matrix is constructed according to the state estimation data of the current sampling frame, and the calibrated point cloud data is transformed from the lidar coordinate system to the global coordinate system by using the pose transformation matrix, where the global coordinate system is the coordinate system where the IMU frame is located at the initial moment of the system. Finally, the point cloud is updated to the global map.

[0089] In a further embodiment, the optimal value of the system state of the current sampling frame k is obtained by using the error-state iterative Kalman filter algorithm , and thus the pose transformation matrix from the system coordinate system of the current sampling frame k to the global coordinate system can be obtained , and according to the matrix the calibrated point cloud data is transformed from the lidar coordinate system to the global coordinate system.

[0090] In a further embodiment, in order to eliminate the event bias between the lidar and the IMU, a hardware synchronization strategy can also be adopted to strictly synchronize the lidar-inertial measurement unit in time.

[0091] The above embodiments propose a method for real-time construction of a global point cloud map, including obtaining the lidar point cloud data and inertial measurement data of the current sampling frame of the target scene, performing distortion correction on the lidar point cloud data of the current sampling frame according to the state estimation data of the previous sampling frame and the inertial measurement data of the current sampling frame to obtain calibrated point cloud data, calculating the state estimation data of the current sampling frame by using the error-state iterative Kalman filter algorithm based on the calibrated point cloud data, and coordinate-transforming the calibrated point cloud data according to the state estimation data of the current sampling frame to generate a real-time global point cloud map of the target scene; the method proposed by the present invention uses inertial measurement data to perform prior prediction on the motion state of the sampling frame and perform point cloud distortion correction, thereby combining the lidar point cloud data and using the error-state iterative Kalman filter algorithm to iteratively correct the state prediction value to make it approach the true value of the system, and finally updating the sampling frame point cloud data to the global point cloud map to realize the real-time and accurate construction of the global point cloud map of the target scene.

[0092] The above-disclosed method can be implemented by various forms of devices. Therefore, the present invention also discloses a device for real-time construction of a global point cloud map corresponding to the above method, and specific embodiments are given below for detailed description.

[0093] As Figure 2 shown, an embodiment of the present invention provides a device for real-time construction of a global point cloud map, including:

[0094] The data acquisition module 202 is configured to acquire the lidar point cloud data and inertial measurement data of the current sampling frame of the target scene;

[0095] The point cloud correction module 204 is configured to perform distortion correction on the lidar point cloud data of the current sampling frame according to the state estimation data of the previous sampling frame and the inertial measurement data of the current sampling frame to obtain corrected point cloud data;

[0096] The state estimation module 206 is configured to calculate the state estimation data of the current sampling frame based on the corrected point cloud data by using the error state iterative Kalman filter algorithm;

[0097] The point cloud map generation module 208 is configured to perform coordinate transformation on the corrected point cloud data according to the state estimation data of the current sampling frame to generate a real-time global point cloud map of the target scene.

[0098] The device provided by the embodiments of the present application has the same implementation principle and the same technical effects as those of the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the device embodiments, reference may be made to the corresponding content in the foregoing method embodiments.

[0099] The methods and related devices mentioned in the foregoing embodiments are described with reference to the method flowcharts and / or structural schematic diagrams provided by the embodiments of the present application. Specifically, each process and / or block of the method flowchart and / or structural schematic diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in one process Figure 1 one process or multiple processes and / or structural schematic Figure 1 one block or multiple blocks. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in one process Figure 1 one process or multiple processes and / or structural schematic Figure 1 one block or multiple blocks. These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or structural schematic one block or multiple blocks.

[0100] The following embodiments are described by taking the application of this method to a computer device as an example. It can be understood that the computer device can be any device with computing and processing functions, and can be, but is not limited to, a server or a personal laptop computer, etc. In one embodiment, the computer device can be an application server, and the application server can be a server for running an application under test.

[0101] Refer to Figure 3 , which shows a hardware structure block diagram of an electronic device. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0102] As Figure 3 shown, the electronic device includes: at least one processor 1, at least one communication interface 2, at least one memory 3, and at least one communication bus 4;

[0103] In the embodiments of the present application, the number of the processor 1, the communication interface 2, the memory 3, and the communication bus 4 is at least one, and the processor 1, the communication interface 2, and the memory 3 complete mutual communication through the communication bus 4;

[0104] The processor 1 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention, etc.;

[0105] The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory;

[0106] Among them, the memory stores a program, and the processor can call the program stored in the memory. The program is used to: implement each processing flow of the foregoing real-time global point cloud map construction solution.

[0107] The embodiments of the present invention also provide a readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements each processing flow of the real-time global point cloud map construction solution provided in the above embodiments and / or any possible implementation manner in combination with the embodiments.

[0108] The above embodiments have described the present invention in particular detail with respect to possible scenarios, and those skilled in the art will recognize that the present invention can be practiced through other embodiments. The specific naming of components, the case of terms, attributes, data structures, or any other programming or structural aspects are not mandatory or significant, and the mechanisms or features for implementing the present invention can have different names, forms, or procedures. The system can be implemented through a combination of hardware and software (as described), entirely through hardware elements, or entirely through software elements. The specific division of functions between the various system components described herein is exemplary and not mandatory; rather, the functions performed by a single system component can be performed by multiple components, or the functions performed by multiple components can be performed by a single component.

[0109] Those skilled in the art should understand that each step of the above-disclosed method can be implemented by a general-purpose computing device, which can be centralized on a single computing device or distributed across a network composed of multiple computing devices. Optionally, they can be implemented with program code executable by the computing device, so that they can be stored in a storage device and executed by the computing device, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. Thus, the disclosure of the embodiments of the present invention is not limited to any specific combination of hardware and software.

[0110] These programs executable by the computing device (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and can implement these computing programs using high-level procedural and / or object-oriented programming languages and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0111] Certain aspects of the present invention include the process steps and instructions described herein in the form of algorithms. It should be noted that the process steps and instructions of the present invention can be implemented in software, firmware, and / or hardware, and when implemented by software, it can be downloaded and thus stored on different platforms used by various operating systems and operated from those platforms.

[0112] Those skilled in the art can understand that the structures shown in the drawings are only block diagrams of some of the structures related to the solution of this application, and do not constitute a limitation on the terminal devices to which the solution of this application is applied. The specific terminal devices may include more or fewer components than those shown in the figures, or combine some components, or have different component arrangements.

[0113] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "possible design", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0114] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, the element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the said element.

[0115] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a global point cloud map in real time, characterized in that: include: Obtain the laser point cloud data and inertial measurement data of the current sampling frame of the target scene; Performing distortion correction on the laser point cloud data of the current sampling frame according to the state estimation data of the previous sampling frame and the inertial measurement data of the current sampling frame to obtain corrected point cloud data; Based on the corrected point cloud data, using an error state iterative Kalman filter algorithm to calculate state estimation data of a current sampling frame; The corrected point cloud data is coordinate-converted according to the state estimation data of the current sampling frame to generate a real-time global point cloud map of the target scene.

2. The method according to claim 1, characterized in that The step of performing distortion correction on the laser point cloud data of the current sampling frame according to the state estimation data of the previous sampling frame and the inertial measurement data of the current sampling frame to obtain the corrected point cloud data comprises: constructing an inertial measurement kinematics model using the inertial measurement data of the current sampling frame; Calculating a priori estimation data of a current sampling frame according to the inertial measurement kinematics model and the state estimation data of the previous sampling frame; The laser point cloud data of the current sampling frame is transformed into a coordinate system using the prior estimation data of the current sampling frame to obtain corrected point cloud data.

3. The method according to claim 2, characterized in that The method of using the prior estimation data of the current sampling frame to transform the laser point cloud data of the current sampling frame into a coordinate system to obtain the corrected point cloud data includes: The laser point cloud data of the current sampling frame is converted from the laser radar coordinate system to the IMU coordinate system to obtain the first point cloud data; the first point cloud data is converted from the IMU coordinate system to the IMU coordinate system at the end time of the current sampling frame using the prior estimation data of the current sampling frame to obtain the second point cloud data; the second point cloud data is converted from the IMU coordinate system at the end time of the current sampling frame to the laser radar coordinate system to obtain the corrected point cloud data.

4. The method according to claim 2, characterized in that: The state estimation data of the current sampling frame includes a system state and an error state, and the state estimation data of the current sampling frame calculated by using the error state iterative Kalman filtering algorithm includes: Calculate the observation residual of the current iteration round according to the corrected point cloud data and the observation model; Calculating the Kalman gain, and calculating the error state of the current iteration round according to the Kalman gain and the observation residual; Calculate the system state of the current iteration round according to the error state of the current iteration round and the prior estimation data of the current sampling frame; When the system state of the current iteration round meets the set convergence condition, the optimal value of the system state is output.

5. The method according to claim 4, characterized in that The observation residual is expressed as follows: in, represents the observation residual of the k-th sampling frame in the i-th iteration round, represents the Jacobian matrix, calculated based on the corrected point cloud data, represents the error state prior estimate of the i-1th iteration round of the kth sampling frame, represents the observation noise, , represents the observation noise The covariance matrix of .

6. The method according to claim 4, characterized in that The setting of the convergence conditions includes: The change of the system state of the current sampling frame relative to the system state of the previous sampling frame is less than the set threshold, or the number of iterations reaches the set value.

7. The method according to claim 1, characterized in that The coordinate transformation of the corrected point cloud data according to the state estimation data of the current sampling frame comprises: Construct a pose transformation matrix based on the state estimation data of the current sampling frame; The correction point cloud data is converted from the laser radar coordinate system to the global coordinate system using the posture transformation matrix, wherein the global coordinate system is the coordinate system where the IMU frame is located at the initial moment of the system.

8. A device for constructing a global point cloud map in real time, characterized in that: include: A data acquisition module is used to acquire laser point cloud data and inertial measurement data of the current sampling frame of the target scene; A point cloud correction module, used to perform distortion correction on the laser point cloud data of the current sampling frame according to the state estimation data of the previous sampling frame and the inertial measurement data of the current sampling frame to obtain corrected point cloud data; A state estimation module, used to calculate state estimation data of a current sampling frame based on the corrected point cloud data using an error state iterative Kalman filter algorithm; The point cloud map generation module is used to perform coordinate transformation on the corrected point cloud data according to the state estimation data of the current sampling frame to generate a real-time global point cloud map of the target scene.

9. An electronic device, characterized in that: The device comprises a memory storing computer executable instructions and a processor, and when the computer executable instructions are executed by the processor, the device executes the real-time construction method of the global point cloud map as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that: A computer executable program is stored, and when the program is executed, the real-time construction method of the global point cloud map as described in any one of claims 1 to 7 can be implemented.

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