A scanning method, device and medium based on walking of people

Abstract

The present invention provides a scanning method based on human walking. The present invention uses a pedestrian walking model to provide the initial value of the three-dimensional laser radar, thereby solving the problem that when a portable three-dimensional laser radar is used for mapping, the robustness of the laser point cloud registration algorithm for acquiring the pose is reduced due to the inability to provide the initial value. The present invention uses a pedestrian walking model to provide the initial value of the three-dimensional laser radar, which can improve the robustness of the portable laser radar when building a map. This provides a possibility for using a portable laser radar for mapping, and further solves the problem of not using a robot to build a map on site.

Description

Technical Field

[0001] The present invention relates to the field of synchronous positioning and mapping, and in particular to a method, device and medium for scanning a map based on human walking. Background Art

[0002] Outdoor autonomous mobile robots require the robot to be able to achieve autonomous path-finding and walking capabilities, and the premise for achieving this capability is that the robot knows its location. Therefore, the positioning technology of autonomous mobile robots has been one of the hot research technologies in recent years. Currently, the positioning methods widely used in outdoor autonomous navigation mobile robots mainly include three-dimensional laser positioning. Patrol robots are a specific application of automatic mobile robots. They are generally equipped with three-dimensional laser radar sensors for positioning and navigation. They are mainly used for patrolling, taking pictures, and recording in parks, buildings, and other places.

[0003] Specifically, the patrol robot realizes positioning and navigation based on a pre-built 3D point cloud map, where the 3D point cloud map expresses the actual environment in three dimensions through a large number of 3D point clouds.

[0004] In the field of patrol robots, the construction of three-dimensional point cloud maps is usually achieved by controlling the patrol robot to walk in actual scenes. The commonly used algorithm is that the wheeled odometer provides the initial pose value of the three-dimensional lidar before and after the robot moves, and then the point cloud registration algorithm performs precise registration based on the existing initial pose value to obtain a higher-precision pose (displacement and angular change, characterizing the robot before and after movement).

[0005] Currently, patrol robots usually use the robot itself to scan the map. However, since robots are not easy to transport, it is not conducive to evaluating the three-dimensional point cloud map of the working scene in advance.

[0006] The background description provided herein is for the purpose of generally presenting the context of the present disclosure. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims of this application and are not admitted to be prior art by inclusion in this section. Summary of the invention

[0007] In view of the above technical problems in the related art, the present invention proposes a scanning method based on human walking, which comprises the following steps:

[0008] S1, obtain the point cloud data of the first and second frames of the 3D laser radar, which are recorded as the first frame Scan_i and the second frame Scan_j respectively;

[0009] S2, according to the timestamp corresponding to the collected point cloud, obtaining the second data of the second IMU installed on the pedestrian's foot between the first frame Scan_i and the second frame Scan_j, and performing gait analysis on the second data to obtain the number of steps K of the pedestrian;

[0010] S3, according to the timestamp corresponding to the collected point cloud, obtaining first data of a first IMU installed on the three-dimensional laser radar between the first frame Scan_i and the second frame Scan_j, and calculating the first data to obtain the three-dimensional rotation angle of the three-dimensional laser radar;

[0011] S4, obtaining the displacement of the laser radar in three directions according to the number of steps of the pedestrian and the three-dimensional turning angle of the three-dimensional laser radar;

[0012] S5. Based on the displacement and rotation angle of the laser radar in three directions, the second frame Scan_j is registered to the initial posture of the first frame Scan_i;

[0013] S6. Use the ICP point cloud registration algorithm to register the second frame Scan_j with the first frame Scan_i to obtain the precise motion posture of the robot, and use the precise posture to splice the point clouds of the first frame Scan_i and the second frame Scan_j.

[0014] Specifically, the acquiring of second data from a second IMU installed on a foot of a pedestrian and performing gait analysis on the second data to acquire displacement data specifically includes:

[0015] S21, obtaining raw data of the second IMU;

[0016] S22, calculating the mean m and standard deviation std of the second IMU three-axis accelerometer within the sliding window;

[0017] S23, when the mean m is less than the first threshold Threshold_a and the standard deviation std is greater than the second threshold Threshold_std, a step taken by the pedestrian is obtained, and the number of steps K of the pedestrian is obtained.

[0018] Specifically, the sliding window is 0.5s.

[0019] Specifically, the first threshold is -15m^2 / s, and the second threshold is 3.0m^2 / s.

[0020] Specifically, the displacement of the laser radar in three directions can be calculated according to the number of steps of the pedestrian and the three turning angles of the laser radar;

[0021] odomX=cos(Yaw)*cos(Pitch)*K*R

[0022] odomY=sin(Yaw)*cos(Pitch)*K*R

[0023] odomZ=sin(Pitch)*K*R

[0024] Among them, Yaw is the yaw angle of the laser radar, Pitch is the pitch angle of the laser radar, and the span displacement R = 0.6 meters for an adult pedestrian to complete one step.

[0025] In a second aspect, another embodiment of the present invention discloses a scanning device based on human walking, which includes the following units:

[0026] The laser point cloud acquisition unit is used to obtain the point cloud data of the front and back two frames of the 3D laser radar, which are recorded as the first frame Scan_i and the second frame Scan_j respectively;

[0027] A pedestrian step acquisition unit, configured to acquire second data of a second IMU installed on a pedestrian's foot between a first frame Scan_i and a second frame Scan_j according to a timestamp corresponding to the collected point cloud, and perform gait analysis on the second data to acquire the pedestrian's step number K;

[0028] The laser radar three-dimensional turning angle acquisition unit is used to acquire the first data of the first IMU installed on the three-dimensional laser radar between the first frame Scan_i and the second frame Scan_j according to the timestamp corresponding to the collected point cloud, and calculate the first data to obtain the three-dimensional turning angle of the three-dimensional laser radar;

[0029] A laser radar displacement acquisition unit, used to acquire the displacement of the laser radar in three directions according to the number of steps of the pedestrian and the three-dimensional turning angle of the three-dimensional laser radar;

[0030] The initial registration unit uses the displacement and rotation angle of the laser radar in three directions as the initial position of the second frame Scan_j to be registered to the first frame Scan_i;

[0031] The mapping unit is used to use the ICP point cloud registration algorithm to register the second frame Scan_j with the first frame Scan_i, obtain the precise motion posture of the robot, and use the precise posture to splice the point clouds of the first frame Scan_i and the second frame Scan_j.

[0032] Specifically, the pedestrian step number acquisition unit also includes:

[0033] A second IMU data acquisition unit, used to acquire raw data of the second IMU;

[0034] A mean value and standard deviation acquisition unit, used for calculating the mean value m and the standard deviation std of the second IMU three-axis accelerometer within the sliding window;

[0035] The pedestrian step calculation unit is used to obtain the pedestrian's step count K when the mean m is less than the first threshold Threshold_a and the standard deviation std is greater than the second threshold Threshold_std.

[0036] Specifically, the sliding window is 0.5s.

[0037] Specifically, the displacement of the laser radar in three directions can be calculated according to the number of steps of the pedestrian and the three turning angles of the laser radar;

[0038] odomX=cos(Yaw)*cos(Pitch)*K*R

[0039] odomY=sin(Yaw)*cos(Pitch)*K*R

[0040] odomZ=sin(Pitch)*K*R

[0041] Among them, Yaw is the yaw angle of the laser radar, Pitch is the pitch angle of the laser radar, and the span displacement R = 0.6 meters for an adult pedestrian to complete one step.

[0042] In a third aspect, another embodiment of the present invention discloses a non-volatile memory having instructions stored thereon, and the processor is used to implement the above-mentioned scanning method based on human walking when executing the instructions.

[0043] The present invention uses a pedestrian walking model to provide the initial value of the three-dimensional laser radar, thereby solving the problem that when a portable three-dimensional laser radar is used for mapping, the robustness of the laser point cloud registration algorithm for acquiring the pose is reduced due to the inability to provide the initial value. The present invention uses a pedestrian walking model to provide the initial value of the three-dimensional laser radar, which can improve the robustness of the portable laser radar when mapping. This provides a possibility for using a portable laser radar for mapping, and further solves the problem of not using a robot to build maps on site. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 is a flow chart of a scanning method based on human walking provided by an embodiment of the present invention;

[0046] Figure 2 is a schematic diagram of a sliding window provided by an embodiment of the present invention;

[0047] Figure 3 is a schematic diagram of a device based on human walking provided by an embodiment of the present invention;

[0048] Figure 4 It is a schematic diagram of a scanning device based on human walking provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0050] Embodiment 1

[0051] The scanning system based on human walking in this embodiment includes a first IMU, a second IMU, and a three-dimensional laser radar. The second IMU in this embodiment is installed on the shoes of pedestrians. The second IMU collects the walking information of pedestrians, such as whether the pedestrians are stationary or walking. The data obtained by the second IMU can be used to analyze the gait of the pedestrians, thereby obtaining the number of steps the pedestrians have taken. The specific analysis of the second IMU data is described below.

[0052] In addition, a 3D laser radar is fixed on the pedestrian. The 3D laser radar of this embodiment can be a small consumer-grade 3D radar, which is convenient for installation on the pedestrian. One implementation method is to install the 3D laser radar on a fixed object, such as a rigid helmet, and then fix the 3D laser radar on the rigid helmet, and the pedestrian wears the rigid helmet. In another implementation method, the 3D laser radar can be fixed in a rigid belt, and the pedestrian wears the rigid belt and fixes it on the pedestrian's chest or abdomen.

[0053] Specifically, the present embodiment further provides a first IMU, which is fixed on the three-dimensional laser radar. The first IMU is used to obtain the pitch angle of the three-dimensional laser radar. The first IMU of the present embodiment is an IMU built into the three-dimensional laser radar. In another embodiment, if the three-dimensional laser radar does not have a built-in first IMU, the first IMU can be arranged on the three-dimensional laser radar.

[0054] refer to Figure 1 This embodiment provides a scanning method based on people walking, which includes the following steps:

[0055] S1, obtain the point cloud data of the first and second frames of the 3D laser radar, which are recorded as the first frame Scan_i and the second frame Scan_j respectively;

[0056] S2, according to the timestamp corresponding to the collected point cloud, obtaining the second data of the second IMU installed on the pedestrian's foot between the first frame Scan_i and the second frame Scan_j, and performing gait analysis on the second data to obtain the number of steps K of the pedestrian;

[0057] Specifically, when the three-dimensional laser radar obtains laser radar data, it will record the corresponding timestamp. This embodiment obtains the time of the first frame Scan_i and the second frame Scan_j according to the corresponding time when the laser frame data is obtained by the three-dimensional laser radar.

[0058] Specifically, this embodiment obtains second data from a second IMU installed on the foot of a pedestrian, and performs gait analysis on the second data to obtain displacement data, specifically including:

[0059] S21, obtaining raw data of the second IMU;

[0060] refer to Figure 2 , input the original data of the three-axis accelerometer of the second IMU. The time for a person to lift and put his foot down when walking lasts about 0.5 seconds respectively. Set a data sliding window with a length of 0.5 seconds and an interval of 0.2 seconds for each sliding.

[0061] S22, calculating the mean m and standard deviation std of the second IMU three-axis accelerometer within the sliding window;

[0062]

[0063]

[0064] where x i is the measurement value of the second IMU three-axis accelerometer; n is the number of measurement data of the second IMU in the sliding window;

[0065] S23, when the mean m is less than the first threshold Threshold_a and the standard deviation std is greater than the second threshold Threshold_std, a step taken by the pedestrian is obtained, and the number of steps K of the pedestrian is obtained;

[0066] When the mean m is less than the first threshold Threshold_a, which is -15m^2 / s in this embodiment, and the standard deviation std is greater than the second threshold Threshold_std, which is 3.0m^2 / s in this embodiment, the pedestrian takes a step; it is considered that the foot has been put down, which means that the pedestrian has taken a step;

[0067] S3, according to the timestamp corresponding to the collected point cloud, obtaining first data of a first IMU installed on the three-dimensional laser radar between the first frame Scan_i and the second frame Scan_j, and calculating the first data to obtain the three-dimensional rotation angle of the three-dimensional laser radar;

[0068] According to the timestamp corresponding to the collected point cloud, the original data of the three-axis gyroscope of the first IMU installed on the 3D laser radar between Scan_i and Scan_j is obtained, and the 3D rotation angle (Yaw Pitch Roll) of the 3D laser radar during this time is calculated according to the formula, where the Roll angle is forced to be 0 because it is generally assumed that pedestrians do not tilt left or right;

[0069]

[0070] Where g is the IMU three-axis gyroscope measurement value; n is the number of the first IMU measurement data between Scan_i and Scan_j, and t is the IMU measurement frequency;

[0071] It should be noted that this embodiment only shows the calculation method for one axis, and the other two axes are similar.

[0072] S4, obtaining the displacement of the laser radar in three directions according to the number of steps of the pedestrian and the three-dimensional turning angle of the three-dimensional laser radar;

[0073] This embodiment can calculate the displacement of the laser radar in three directions according to the number of steps of the pedestrian and the three turning angles of the laser radar;

[0074] odomX=cos(Yaw)*cos(Pitch)*K*R

[0075] odomY=sin(Yaw)*cos(Pitch)*K*R

[0076] odomZ=sin(Pitch)*K*R

[0077] Among them, Yaw is the yaw angle of the laser radar, and Pitch is the pitch angle of the laser radar.

[0078] This embodiment assumes that the span displacement R≈0.6 meters required for an adult pedestrian to complete one step.

[0079] S5. Based on the displacement and rotation angle of the laser radar in three directions, the second frame Scan_j is registered to the initial posture of the first frame Scan_i;

[0080] S6. Use the ICP point cloud registration algorithm to register the second frame Scan_j with the first frame Scan_i to obtain the precise motion posture of the robot, and use the precise posture to splice the point clouds of the first frame Scan_i and the second frame Scan_j.

[0081] The point cloud Scan_i before the robot moves is recorded as target, and the point cloud Scan_j after the robot moves is recorded as source;

[0082] The ICP point cloud registration algorithm is used to align the target and source to obtain the precise motion posture of the robot. The two frames of point cloud are spliced ​​using the precise posture to complete the map construction.

[0083] This embodiment uses a pedestrian walking model to provide the initial value of the three-dimensional laser radar, thereby solving the problem that when using a portable three-dimensional laser radar for mapping, the robustness of acquiring the position and posture using the laser point cloud registration algorithm is reduced due to the inability to provide the initial value. This embodiment uses a pedestrian walking model to provide the initial value of the three-dimensional laser radar to improve the robustness of the portable laser radar when mapping. This provides a possibility for using a portable laser radar for mapping, and can also solve the problem of not using a robot to build maps on site.

[0084] Implementation II

[0085] refer to Figure 3 , this embodiment provides a scanning device based on human walking, which includes the following units:

[0086] The laser point cloud acquisition unit is used to obtain the point cloud data of the front and back two frames of the 3D laser radar, which are recorded as the first frame Scan_i and the second frame Scan_j respectively;

[0087] A pedestrian step acquisition unit, configured to acquire second data of a second IMU installed on a pedestrian's foot between a first frame Scan_i and a second frame Scan_j according to a timestamp corresponding to the collected point cloud, and perform gait analysis on the second data to acquire the pedestrian's step number K;

[0088] Specifically, when the three-dimensional laser radar obtains laser radar data, it will record the corresponding timestamp. This embodiment obtains the time of the first frame Scan_i and the second frame Scan_j according to the corresponding time when the laser frame data is obtained by the three-dimensional laser radar.

[0089] Specifically, the pedestrian step number obtaining unit in this embodiment further includes:

[0090] A second IMU data acquisition unit, used to acquire raw data of the second IMU;

[0091] Input the raw data of the three-axis accelerometer of the second IMU. The time for a person to lift and put his foot down when walking lasts about 0.5 seconds respectively. Set a data sliding window with a length of 0.5 seconds and an interval of 0.2 seconds for each sliding.

[0092] A mean value and standard deviation acquisition unit, used for calculating the mean value m and the standard deviation std of the second IMU three-axis accelerometer within the sliding window;

[0093]

[0094]

[0095] where x i is the measurement value of the second IMU three-axis accelerometer; n is the number of measurement data of the second IMU in the sliding window;

[0096] A pedestrian step calculation unit, used for obtaining a pedestrian's step count K when the mean m is less than a first threshold Threshold_a and the standard deviation std is greater than a second threshold Threshold_std;

[0097] When the mean m is less than the first threshold Threshold_a, which is -15m^2 / s in this embodiment, and the standard deviation std is greater than the second threshold Threshold_std, which is 3.0m^2 / s in this embodiment, the pedestrian takes a step; it is considered that the foot has been put down, which means that the pedestrian has taken a step;

[0098] The laser radar three-dimensional turning angle acquisition unit is used to acquire the first data of the first IMU installed on the three-dimensional laser radar between the first frame Scan_i and the second frame Scan_j according to the timestamp corresponding to the collected point cloud, and calculate the first data to obtain the three-dimensional turning angle of the three-dimensional laser radar;

[0099] According to the timestamp corresponding to the collected point cloud, the original data of the three-axis gyroscope of the first IMU installed on the 3D laser radar between Scan_i and Scan_j is obtained, and the 3D rotation angle (Yaw Pitch Roll) of the 3D laser radar during this time is calculated according to the formula, where the Roll angle is forced to be 0 because it is generally assumed that pedestrians do not tilt left or right;

[0100]

[0101] Where g is the IMU three-axis gyroscope measurement value; n is the number of the first IMU measurement data between Scan_i and Scan_j, and t is the IMU measurement frequency;

[0102] It should be noted that this embodiment only shows the calculation method for one axis, and the other two axes are similar.

[0103] A laser radar displacement acquisition unit, used to acquire the displacement of the laser radar in three directions according to the number of steps of the pedestrian and the three-dimensional turning angle of the three-dimensional laser radar;

[0104] This embodiment can calculate the displacement of the laser radar in three directions according to the number of steps of the pedestrian and the three turning angles of the laser radar;

[0105] odomX=cos(Yaw)*cos(Pitch)*K*R

[0106] odomY=sin(Yaw)*cos(Pitch)*K*R

[0107] odomZ=sin(Pitch)*K*R

[0108] Among them, Yaw is the yaw angle of the laser radar, and Pitch is the pitch angle of the laser radar.

[0109] This embodiment assumes that the span displacement R≈0.6 meters required for an adult pedestrian to complete one step.

[0110] The initial registration unit uses the displacement and rotation angle of the laser radar in three directions as the initial position of the second frame Scan_j to be registered to the first frame Scan_i;

[0111] The mapping unit is used to use the ICP point cloud registration algorithm to register the second frame Scan_j with the first frame Scan_i, obtain the precise motion posture of the robot, and use the precise posture to splice the point clouds of the first frame Scan_i and the second frame Scan_j.

[0112] The point cloud Scan_i before the robot moves is recorded as target, and the point cloud Scan_j after the robot moves is recorded as source;

[0113] The ICP point cloud registration algorithm is used to align the target and source to obtain the precise motion posture of the robot. The two frames of point cloud are spliced ​​using the precise posture to complete the map construction.

[0114] This embodiment uses a pedestrian walking model to provide the initial value of the three-dimensional laser radar, thereby solving the problem that when using a portable three-dimensional laser radar for mapping, the robustness of acquiring the position and posture using the laser point cloud registration algorithm is reduced due to the inability to provide the initial value. This embodiment uses a pedestrian walking model to provide the initial value of the three-dimensional laser radar to improve the robustness of the portable laser radar when mapping. This provides a possibility for using a portable laser radar for mapping, and can also solve the problem of not using a robot to build maps on site.

[0115] Embodiment 3

[0116] refer to Figure 4 , Figure 4: is a structural schematic diagram of a scanning device based on walking of a person in this embodiment. The scanning device based on walking of a person 20 in this embodiment includes a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, the steps in the above method embodiment are implemented. Alternatively, when the processor 21 executes the computer program, the functions of each module / unit in the above device embodiments are implemented.

[0117] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the image scanning device 20 based on walking personnel. For example, the computer program may be divided into the modules in Embodiment 2. For the specific functions of each module, please refer to the working process of the device described in the above embodiment, which will not be repeated here.

[0118] The image scanning device 20 based on human walking may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will appreciate that the schematic diagram is merely an example of the image scanning device 20 based on human walking, and does not constitute a limitation on the image scanning device 20 based on human walking, and may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, the image scanning device 20 based on human walking may also include input and output devices, network access devices, buses, etc.

[0119] The processor 21 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 21 is the control center of the scanning device 20 based on walking people, and uses various interfaces and lines to connect various parts of the scanning device 20 based on walking people.

[0120] The memory 22 can be used to store the computer program and / or module. The processor 21 realizes various functions of the image scanning device 20 based on walking by running or executing the computer program and / or module stored in the memory 22, and calling the data stored in the memory 22. The memory 22 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 22 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0121] Wherein, if the module / unit integrated in the scanning device 20 based on walking of a person is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor 21. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0122] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0123] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A scanning method based on human walking, comprising the following steps: S1, obtain the point cloud data of the first and second frames of the 3D laser radar, which are recorded as the first frame Scan_i and the second frame Scan_j respectively; S2, according to the timestamp corresponding to the collected point cloud, obtaining the second data of the second IMU installed on the pedestrian's foot between the first frame Scan_i and the second frame Scan_j, and performing gait analysis on the second data to obtain the number of steps K of the pedestrian; S3, according to the timestamp corresponding to the collected point cloud, obtaining first data of a first IMU installed on the three-dimensional laser radar between the first frame Scan_i and the second frame Scan_j, and calculating the first data to obtain the three-dimensional rotation angle of the three-dimensional laser radar; S4, obtaining the displacement of the laser radar in three directions according to the number of steps of the pedestrian and the three-dimensional turning angle of the three-dimensional laser radar; S5. Based on the displacement and rotation angle of the laser radar in three directions, the second frame Scan_j is registered to the initial posture of the first frame Scan_i; S6. Use the ICP point cloud registration algorithm to register the second frame Scan_j with the first frame Scan_i to obtain the precise motion posture of the robot, and use the precise posture to splice the point clouds of the first frame Scan_i and the second frame Scan_j.

2. The method according to claim 1, wherein obtaining second data from a second IMU installed on the foot of the pedestrian and performing gait analysis on the second data to obtain displacement data specifically comprises: S21, obtaining raw data of the second IMU; S22, calculating the mean m and standard deviation std of the second IMU three-axis accelerometer within the sliding window; S23, when the mean m is less than the first threshold Threshold_a and the standard deviation std is greater than the second threshold Threshold_std, a step taken by the pedestrian is obtained, and the number of steps K of the pedestrian is obtained.

3. According to the method of claim 2, the sliding window is 0.5s.

4. The method according to claim 3, wherein the first threshold is -15 m^2 / s and the second threshold is 3.0 m^2 / s.

5. According to the method of claim 4, the displacement of the laser radar in three directions can be calculated according to the number of steps of the pedestrian and the three turning angles of the laser radar; odomX=cos(Yaw)*cos(Pitch)*K*R odomY=sin(Yaw)*cos(Pitch)*K*R odomZ=sin(Pitch)*K*R in, Yaw is the yaw angle of the laser radar, and Pitch is the pitch angle of the laser radar. The span displacement R of an adult pedestrian completing one step is 0.6 meters.

6. A scanning device based on human walking, comprising the following units: The laser point cloud acquisition unit is used to obtain the point cloud data of the first and second frames of the 3D laser radar, which are recorded as the first frame Scan_i and the second frame Scan_j respectively; A pedestrian step acquisition unit, configured to acquire second data of a second IMU installed on a pedestrian's foot between a first frame Scan_i and a second frame Scan_j according to a timestamp corresponding to the collected point cloud, and perform gait analysis on the second data to acquire the pedestrian's step number K; The laser radar three-dimensional turning angle acquisition unit is used to acquire the first data of the first IMU installed on the three-dimensional laser radar between the first frame Scan_i and the second frame Scan_j according to the timestamp corresponding to the collected point cloud, and calculate the first data to obtain the three-dimensional turning angle of the three-dimensional laser radar; A laser radar displacement acquisition unit, used to acquire the displacement of the laser radar in three directions according to the number of steps of the pedestrian and the three-dimensional turning angle of the three-dimensional laser radar; The initial registration unit uses the displacement and rotation angle of the laser radar in three directions as the initial position of the second frame Scan_j to be registered to the first frame Scan_i; The mapping unit is used to use the ICP point cloud registration algorithm to register the second frame Scan_j with the first frame Scan_i, obtain the precise motion posture of the robot, and use the precise posture to splice the point clouds of the first frame Scan_i and the second frame Scan_j.

7. The device according to claim 6, the pedestrian step acquisition unit further comprising: A second IMU data acquisition unit, used to acquire raw data of the second IMU; A mean value and standard deviation acquisition unit, used for calculating the mean value m and the standard deviation std of the second IMU three-axis accelerometer within the sliding window; The pedestrian step calculation unit is used to obtain the pedestrian's step count K when the mean m is less than the first threshold Threshold_a and the standard deviation std is greater than the second threshold Threshold_std. The device according to claim 7 , wherein the sliding window is 0.5 s.

9. The device according to claim 8, wherein the displacement of the laser radar in three directions can be calculated according to the number of steps of the pedestrian and the three turning angles of the laser radar; odomX=cos(Yaw)*cos(Pitch)*K*R odomY=sin(Yaw)*cos(Pitch)*K*R odomZ=sin(Pitch)*K*R in, Yaw is the yaw angle of the laser radar, and Pitch is the pitch angle of the laser radar. The span displacement R of an adult pedestrian completing one step is 0.6 meters.

10. A non-volatile memory having instructions stored thereon, wherein a processor, when executing the instructions, is used to implement the image scanning method based on human walking as described in any one of claims 1-5.

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