A positioning method, device, robot and medium for integrating lane lines

Abstract

The present invention provides a positioning method for integrating lane lines, S1, obtaining any two frames of laser radar point cloud data during the robot's walking process, recorded as the first frame and the second frame; S2, obtaining the first point cloud and the second point cloud containing the dotted line and the solid line of the road in the first frame and the second frame; S3, using the point cloud registration algorithm to register the first point cloud and the second point cloud to obtain the first displacement; using the point cloud registration algorithm to register the first frame and the second frame to obtain the second displacement; S4, fusing the first displacement and the second displacement by weighted average to obtain the robot displacement. The present invention obtains the lane line of the road through laser reflectivity, and obtains the displacement positioning result based on the lane line according to the point cloud frame containing only the lane line, and then fuses it with the displacement positioning result based on the laser point cloud to obtain the final displacement. The method of the present invention only needs to obtain the dotted line on the road and realize the point cloud, and does not need to add high-precision GPS equipment to achieve relatively high positioning results.

Description

Technical Field

[0001] The present invention relates to the field of robotics, and in particular to a positioning method, device, robot and medium for fusion lane lines. Background Art

[0002] Autonomous mobile robots require the robot to be able to achieve autonomous path-finding and walking capabilities. The premise for achieving this capability is that the robot knows where it is. 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 autonomous mobile robots include laser point cloud positioning and GPS positioning.

[0003] The working principle of LiDAR is to use laser as the signal source. The pulsed laser emitted by the laser hits trees, roads, bridges, buildings, etc. The light waves will be reflected to the LiDAR receiver, and the reflectivity of the target object will be obtained based on the reflection intensity (mainly depends on the color of the target object). The distance from the LiDAR to the target point is calculated based on the laser ranging principle. The pulsed laser continuously scans the target object, and the data and reflectivity of all target points on the target object can be obtained, and finally output in the laser point cloud format.

[0004] The patrol robot works in complex scenarios, including campus scenarios, park scenarios, square scenarios, road scenarios, etc. When facing road scenarios in large campuses, the laser point cloud contours are sparse and the GPS signal is easily blocked, which will inevitably lead to a decrease in positioning accuracy and affect the normal operation of the patrol robot.

[0005] The principle of laser radar positioning is to directly use the two laser point cloud frames before and after the robot moves for registration to obtain the robot's posture before and after the movement, where the posture includes displacement and angle.

[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 positioning method for fusion lane lines, which comprises the following steps:

[0008] S1, obtain any two frames of laser radar point cloud data during the robot's walking process, recorded as the first frame Scan_i and the second frame Scan_j;

[0009] S2, obtaining the first point cloud source and the second point cloud target including the dotted line and the solid line of the road in the first frame Scan_i and the second frame Scan_j;

[0010] S3, using the point cloud registration algorithm to register the first point cloud with the second point cloud, and obtaining a first displacement pose_line; using the point cloud registration algorithm to register the first frame with the second frame, and obtaining a second displacement pose_image;

[0011] S4. Fusing the first displacement pose_line and the second displacement pose_image by weighted average to obtain the robot displacement pose_robot.

[0012] Specifically, the step S2 also includes: retaining the point clouds with reflectivity within a first preset range for the first frame Scan_i and the second frame Scan_j to obtain the first point cloud and the second point cloud, wherein the first preset range is used to represent the reflectivity of the dotted line and the solid line of the road.

[0013] Specifically, the step S2 further includes: clustering the first point cloud and the second point cloud, and retaining point clouds with a point cloud cluster number greater than a threshold.

[0014] Specifically, before retaining the point clouds with reflectivity within the first preset range for the first frame Scan_i and the second frame Scan_j to obtain the first point cloud and the second point cloud, the method further includes:

[0015] The first frame Scan_i and the second frame Scan_j are height-filtered to retain the first laser point cloud and the second laser point cloud whose heights are within a second preset range.

[0016] Point clouds with reflectivity within a first preset range are retained for the first laser point cloud and the second frame laser point cloud to obtain a first point cloud and a second point cloud, wherein the first preset range is used to represent the reflectivity of the dotted line and the solid line of the road.

[0017] Specifically, the step S4 includes: fusing the first displacement pose_line and the second displacement pose_image by weighted average to obtain the robot displacement pose_robot;

[0018] ;

[0019] in w1 : The weight value of the second displacement, w2 : The weight value of the first displacement; w2>w1 .

[0020] In a second aspect, another embodiment of the present invention discloses a positioning device for fusion lane lines, which includes the following units:

[0021] The laser point cloud frame acquisition unit is used to acquire any two frames of laser radar point cloud data during the robot's walking process, recorded as the first frame Scan_i and the second frame Scan_j;

[0022] A road dotted line and solid line point cloud acquisition unit, used to acquire a first point cloud source and a second point cloud target including a road dotted line and a solid line in the first frame Scan_i and the second frame Scan_j;

[0023] A displacement positioning unit is used to register the first point cloud with the second point cloud using a point cloud registration algorithm to obtain a first displacement pose_line; and to register the first frame with the second frame using a point cloud registration algorithm to obtain a second displacement pose;

[0024] The displacement fusion positioning unit is used to fuse the first displacement pose_line and the second displacement pose_image through weighted average to obtain the robot displacement pose_robot.

[0025] Specifically, the road dotted line and solid line point cloud acquisition unit also includes: retaining the point cloud with reflectivity within a first preset range for the first frame Scan_i and the second frame Scan_j to obtain the first point cloud and the second point cloud, wherein the first preset range is used to represent the reflectivity of the road dotted line and the solid line.

[0026] Specifically, the road dotted line and solid line point cloud acquisition unit further includes: clustering the first point cloud and the second point cloud, and retaining point clouds with a point cloud cluster number greater than a threshold.

[0027] Specifically, before retaining the point clouds with reflectivity within the first preset range for the first frame Scan_i and the second frame Scan_j to obtain the first point cloud and the second point cloud, the method further includes:

[0028] The first frame Scan_i and the second frame Scan_j are height-filtered to retain the first laser point cloud and the second laser point cloud whose heights are within a second preset range.

[0029] Point clouds with reflectivity within a first preset range are retained for the first laser point cloud and the second frame laser point cloud to obtain a first point cloud and a second point cloud, wherein the first preset range is used to represent the reflectivity of the dotted line and the solid line of the road.

[0030] Specifically, the displacement fusion positioning unit fuses the first displacement pose_line and the second displacement pose_image by weighted average to obtain the robot displacement pose_robot;

[0031] ;

[0032] in w1 : The weight value of the second displacement, w2 : The weight value of the first displacement; w2>w1 .

[0033] In a third aspect, another embodiment of the present invention discloses a robot, comprising a central processing unit, a memory, and a laser radar, wherein the memory stores instructions, and the processor is used to implement the above-mentioned positioning method for fused lane lines when executing the instructions.

[0034] In a fourth 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 method for positioning a fused lane line when executing the instructions.

[0035] The present invention first obtains the lane lines of the road through laser reflectivity, and obtains the displacement positioning results based on the lane lines according to the point cloud frames containing only the lane lines, and then fuses them with the displacement positioning results based on the laser point cloud to obtain the final displacement. The positioning method of the fused lane lines of the present invention only needs to obtain the dotted and solid line point clouds on the road, and can achieve relatively high positioning results without adding high-precision GPS equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] 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.

[0037] Figure 1 is a schematic diagram of objects with different reflectivity provided by an embodiment of the present invention;

[0038] Figure 2 is a schematic diagram of laser point clouds of objects with different reflectivities provided by an embodiment of the present invention;

[0039] Figure 3 is a road schematic diagram provided by an embodiment of the present invention;

[0040] Figure 4 This is a positioning method flow of a fusion lane line provided by an embodiment of the present invention;

[0041] Figure 5 is a schematic diagram of a positioning device for integrating lane lines provided in an embodiment of the present invention;

[0042] Figure 6 It is a schematic diagram of a positioning device integrating lane lines provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] 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. Embodiment 1

[0044] LiDAR uses laser as its signal source. The pulsed laser emitted by the laser hits trees, roads, bridges, buildings, etc. The light waves will be reflected to the LiDAR receiver, and the reflectivity of the target object can be obtained based on the reflection intensity (mainly depends on the color of the target object). The distance from the LiDAR to the target point is calculated based on the laser ranging principle. The pulsed laser continuously scans the target object, and the data and reflectivity of all target points on the target object can be obtained, and finally output in the laser point cloud format.

[0045] refer to Figure 1 , Figure 1 Schematic diagram of objects with different reflectivity. The reflectivity from left to right is: tire 5%, high reflective sign (black car logo) 20%, black and white checkered plate 90% and 10%, gray striped plate, reflectivity 100%, 50%, 1.7%.

[0046] Figure 2 yes Figure 1 Laser point cloud image, from Figure 2 It can be seen that the reflectivity of different objects has different intensities in the laser point cloud image, which is manifested in the point cloud image as different reflectivities of corresponding points.

[0047] The robot of this embodiment is equipped with a laser radar, which can obtain laser point cloud data of the environment in real time during the movement of the robot.

[0048] refer to Figure 3 Generally, roads contain lane lines, which are divided into dashed lines and solid lines. Figure 3 . Figure 3 This is a road scene. Generally, the middle of the road in a road scene is a dotted line, and the two sides are solid lines. This embodiment does not limit whether the dotted line or the solid line is a white line or a yellow line. Therefore, when the laser radar point cloud is scanned on the ground, the reflectivity of the solid line and the dotted line on the road is different, and they can be easily distinguished from the laser point cloud.

[0049] refer to Figure 4 This embodiment discloses a positioning method for fusion lane lines, which includes the following steps:

[0050] S1, obtain any two frames of laser radar point cloud data during the robot's walking process, recorded as the first frame Scan_i and the second frame Scan_j;

[0051] The robot of this embodiment is equipped with a laser radar, and the robot can perform autonomous navigation according to the laser radar when working. Specifically, when patrolling, the robot of this embodiment can autonomously walk along the planned route, or can be manually controlled to walk, such as receiving control instructions from a controller. Preferably, the robot of this embodiment generally performs autonomous navigation along the planned route.

[0052] In this embodiment, any two frames of laser radar point cloud data are two consecutive frames of laser point cloud data. In one embodiment, the time of acquiring the first frame Scan_i is earlier than the time of acquiring the second frame Scan_j. In another embodiment, the time of acquiring the first frame Scan_i is later than the time of acquiring the second frame Scan_j.

[0053] S2, obtaining the first point cloud source and the second point cloud target including the dotted line and the solid line of the road in the first frame Scan_i and the second frame Scan_j;

[0054] Specifically, step S2 also includes: retaining point clouds with reflectivity within a first preset range for the first frame Scan_i and the second frame Scan_j to obtain a first point cloud and a second point cloud, wherein the first preset range is used to represent the reflectivity of the dotted line and the solid line of the road.

[0055] In one embodiment, a laser radar may be used to scan the road in advance to obtain laser point cloud data, manually mark the point cloud corresponding to the road, and then obtain the reflectivity A of the virtual and real lines.

[0056] Specifically, for the first frame Scan_i and the second frame Scan_j, point clouds with reflectivity within a first preset range are retained, and the first preset range is [A-10, A+10].

[0057] After obtaining the first point cloud containing the dotted and solid lines of the road, some other point clouds with similar emissivity to the dotted and solid lines of the road are also retained. However, the dotted and solid lines of the road are generally long and contain a relatively large number of point clouds. Therefore, this implementation also removes noise from the first point cloud and the second point cloud based on the number of point clouds.

[0058] Specifically, clustering the first point cloud and the second point cloud, and retaining point clouds with a point cloud cluster number greater than a threshold;

[0059] Specifically, the threshold in this embodiment is 30.

[0060] Specifically, before retaining the point clouds with reflectivity within the first preset range for the first frame Scan_i and the second frame Scan_j to obtain the first point cloud and the second point cloud, the method further includes:

[0061] The first frame Scan_i and the second frame Scan_j are height-filtered to retain the first laser point cloud and the second laser point cloud whose heights are within a second preset range.

[0062] Point clouds with reflectivity within a first preset range are retained for the first laser point cloud and the second frame laser point cloud to obtain a first point cloud and a second point cloud, wherein the first preset range is used to represent the reflectivity of the dotted line and the solid line of the road.

[0063] Specifically, the second preset range of this embodiment is [H-0.5, H+0.5], where H is the height of the laser radar installed on the robot from the ground.

[0064] S3, using the point cloud registration algorithm to register the first point cloud with the second point cloud, and obtaining a first displacement pose_line; using the point cloud registration algorithm to register the first frame with the second frame, and obtaining a second displacement pose_image;

[0065] Specifically, this embodiment uses an iterative closest point algorithm ICP (Iterative Closest Point) to register the first point cloud and the second point cloud to obtain a first displacement pose_line. Similarly, the ICP point cloud registration algorithm is used to register the first frame and the second frame to obtain a second displacement pose_image.

[0066] S4, fusing the first displacement pose_line and the second displacement pose_image by weighted average to obtain the robot displacement pose_robot;

[0067] ;

[0068] w1 : The weight value of the second displacement, wherein the second displacement weight is a weight value based on direct laser point cloud frame registration positioning;

[0069] w2 : The weight value of the first displacement, wherein the weight value of the first displacement is a weight value based on the lane line point cloud registration positioning on the road;

[0070] In one embodiment, wherein w2>w1 ;

[0071] This embodiment first obtains the lane lines of the road through laser reflectivity, and obtains the displacement positioning results based on the lane lines according to the point cloud frames containing only the lane lines, and then fuses them with the displacement positioning results based on the laser point cloud to obtain the final displacement. The positioning method of this embodiment only needs to obtain the dotted and solid line point clouds on the road, and can achieve relatively high positioning results without adding high-precision GPS equipment. Embodiment 2

[0072] refer to Figure 5 , a positioning device for integrating lane lines, comprising the following units:

[0073] The laser point cloud frame acquisition unit is used to acquire any two frames of laser radar point cloud data during the robot's walking process, recorded as the first frame Scan_i and the second frame Scan_j;

[0074] The robot of this embodiment is equipped with a laser radar, and the robot can perform autonomous navigation according to the laser radar when working. Specifically, when patrolling, the robot of this embodiment can autonomously walk along the planned route, or can be manually controlled to walk, such as receiving control instructions from a controller. Preferably, the robot of this embodiment generally performs autonomous navigation along the planned route.

[0075] In this embodiment, any two frames of laser radar point cloud data are two consecutive frames of laser point cloud data. In one embodiment, the time of acquiring the first frame Scan_i is earlier than the time of acquiring the second frame Scan_j. In another embodiment, the time of acquiring the first frame Scan_i is later than the time of acquiring the second frame Scan_j.

[0076] A road dotted line and solid line point cloud acquisition unit, used to acquire a first point cloud source and a second point cloud target including a road dotted line and a solid line in the first frame Scan_i and the second frame Scan_j;

[0077] Specifically, the road dotted line and solid line point cloud acquisition unit also includes: retaining the point cloud with reflectivity within a first preset range for the first frame Scan_i and the second frame Scan_j to obtain the first point cloud and the second point cloud, wherein the first preset range is used to represent the reflectivity of the road dotted line and the solid line.

[0078] In one embodiment, a laser radar may be used to scan the road in advance to obtain laser point cloud data, manually mark the point cloud corresponding to the road, and then obtain the reflectivity A of the virtual and real lines.

[0079] Specifically, for the first frame Scan_i and the second frame Scan_j, point clouds with reflectivity within a first preset range are retained, and the first preset range is [A-10, A+10].

[0080] After obtaining the first point cloud containing the dotted and solid lines of the road, some other point clouds with similar emissivity to the dotted and solid lines of the road are also retained. However, the dotted and solid lines of the road are generally long and contain a relatively large number of point clouds. Therefore, this implementation also removes noise from the first point cloud and the second point cloud based on the number of point clouds.

[0081] Specifically, clustering the first point cloud and the second point cloud, and retaining point clouds with a point cloud cluster number greater than a threshold;

[0082] Specifically, the threshold in this embodiment is 30.

[0083] Specifically, before retaining the point clouds with reflectivity within the first preset range for the first frame Scan_i and the second frame Scan_j to obtain the first point cloud and the second point cloud, the method further includes:

[0084] The first frame Scan_i and the second frame Scan_j are height-filtered to retain the first laser point cloud and the second laser point cloud whose heights are within a second preset range.

[0085] Point clouds with reflectivity within a first preset range are retained for the first laser point cloud and the second frame laser point cloud to obtain a first point cloud and a second point cloud, wherein the first preset range is used to represent the reflectivity of the dotted line and the solid line of the road.

[0086] Specifically, the second preset range of this embodiment is [H-0.5, H+0.5], where H is the height of the laser radar installed on the robot from the ground.

[0087] A displacement positioning unit is used to register the first point cloud with the second point cloud using a point cloud registration algorithm to obtain a first displacement pose_line; and to register the first frame with the second frame using a point cloud registration algorithm to obtain a second displacement pose_image;

[0088] Specifically, this embodiment uses an iterative closest point algorithm ICP (Iterative Closest Point) to register the first point cloud and the second point cloud to obtain a first displacement pose_line. Similarly, the ICP point cloud registration algorithm is used to register the first frame and the second frame to obtain a second displacement pose_image.

[0089] A displacement fusion positioning unit, used for fusing the first displacement pose_line and the second displacement pose_image by weighted average to obtain a robot displacement pose_robot;

[0090] ;

[0091] w1 : The weight value of the second displacement, wherein the second displacement weight is a weight value based on direct laser point cloud frame registration positioning;

[0092] w2 : The weight value of the first displacement, wherein the weight value of the first displacement is a weight value based on the lane line point cloud registration positioning on the road;

[0093] In one embodiment, wherein w2>w1 ;

[0094] This embodiment first obtains the lane lines of the road through laser reflectivity, and obtains the displacement positioning results based on the lane lines according to the point cloud frames containing only the lane lines, and then fuses them with the displacement positioning results based on the laser point cloud to obtain the final displacement. The positioning method of this embodiment only needs to obtain the dotted and solid line point clouds on the road, and can achieve relatively high positioning results without adding high-precision GPS equipment. Embodiment 3

[0095] refer to Figure 6 , Figure 6 : is a structural diagram of a positioning device for fusion lane lines of this embodiment. The positioning device 20 for fusion lane lines of 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.

[0096] 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 positioning device 20 for fusion lane lines. 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.

[0097] The positioning device 20 for fusion lane lines 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 positioning device 20 for fusion lane lines, and does not constitute a limitation on the positioning device 20 for fusion lane lines, and may include more or fewer components than shown in the figure, or combine certain components, or different components, for example, the positioning device 20 for fusion lane lines may also include input and output devices, network access devices, buses, etc.

[0098] 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 gates 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 positioning device 20 for the fused lane line, and uses various interfaces and lines to connect various parts of the positioning device 20 for the fused lane line.

[0099] The memory 22 can be used to store the computer program and / or module. The processor 21 realizes various functions of the positioning device 20 for fusion lane lines 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.

[0100] Wherein, if the module / unit integrated in the positioning device 20 for integrating lane lines 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. When the computer program is executed by the processor 21, the steps of the above-mentioned method embodiments can be implemented. 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. The computer-readable medium may 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.

[0101] 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.

[0102] 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 method for positioning a fused lane line, comprising the following steps: S1, obtain any two frames of laser radar point cloud data during the robot's walking process, recorded as the first frame Scan_i and the second frame Scan_j; S2, obtaining the first point cloud source and the second point cloud target containing the dotted line and the solid line of the road in the first frame Scan_i and the second frame Scan_j; step S2 also includes: Retaining point clouds with reflectivity within a first preset range for the first frame Scan_i and the second frame Scan_j to obtain a first point cloud and a second point cloud, wherein the first preset range is used to represent the reflectivity of the dotted line and the solid line of the road; S3, using the point cloud registration algorithm to register the first point cloud with the second point cloud, and obtaining a first displacement pose_line; using the point cloud registration algorithm to register the first frame with the second frame, and obtaining a second displacement pose_image; S4. Fusing the first displacement pose_line and the second displacement pose_image by weighted average to obtain the robot displacement pose_robot.

2. According to the method of claim 1, the step S2 further comprises: The first point cloud and the second point cloud are clustered, and point clouds having a point cloud cluster number greater than a threshold are retained.

3. The method according to claim 2, before retaining the point clouds with reflectivity within the first preset range for the first frame Scan_i and the second frame Scan_j to obtain the first point cloud and the second point cloud, further comprising: Performing height filtering on the first frame Scan_i and the second frame Scan_j, and retaining the first laser point cloud and the second laser point cloud whose heights are within a second preset range; Point clouds with reflectivity within a first preset range are retained for the first laser point cloud and the second frame laser point cloud to obtain a first point cloud and a second point cloud, wherein the first preset range is used to represent the reflectivity of the dotted line and the solid line of the road.

4. According to the method of claim 3, step S4 comprises: The first displacement pose_line and the second displacement pose_image are fused by weighted average to obtain the robot displacement pose_robot; ； in w1 : The weight value of the second displacement, w2 : The weight value of the first displacement; w2>w1 .

5. A positioning device for integrating lane lines, comprising the following units: The laser point cloud frame acquisition unit is used to acquire any two frames of laser radar point cloud data during the robot's walking process, recorded as the first frame Scan_i and the second frame Scan_j; A road dotted line and solid line point cloud acquisition unit is used to acquire the first point cloud source and the second point cloud target containing the road dotted line and solid line in the first frame Scan_i and the second frame Scan_j; the road dotted line and solid line point cloud acquisition unit also includes: Retaining point clouds with reflectivity within a first preset range for the first frame Scan_i and the second frame Scan_j to obtain a first point cloud and a second point cloud, wherein the first preset range is used to represent the reflectivity of the dotted line and the solid line of the road; A displacement positioning unit is used to register the first point cloud with the second point cloud using a point cloud registration algorithm to obtain a first displacement pose_line; and to register the first frame with the second frame using a point cloud registration algorithm to obtain a second displacement pose_image; The displacement fusion positioning unit is used to fuse the first displacement pose_line and the second displacement pose_image through weighted average to obtain the robot displacement pose_robot.

6. The device according to claim 5, wherein the road dotted line and solid line point cloud acquisition unit further comprises: The first point cloud and the second point cloud are clustered, and point clouds having a point cloud cluster number greater than a threshold are retained.

7. The device according to claim 6, before retaining the point clouds with reflectivity within the first preset range for the first frame Scan_i and the second frame Scan_j to obtain the first point cloud and the second point cloud, further comprising: Performing height filtering on the first frame Scan_i and the second frame Scan_j, and retaining the first laser point cloud and the second laser point cloud whose heights are within a second preset range; Point clouds with reflectivity within a first preset range are retained for the first laser point cloud and the second frame laser point cloud to obtain a first point cloud and a second point cloud, wherein the first preset range is used to represent the reflectivity of the dotted line and the solid line of the road.

8. A robot comprising a central processing unit, a memory, and a laser radar, wherein the memory stores instructions, and the processor is used to implement a positioning method for fused lane lines as described in any one of claims 1-4 when executing the instructions.

9. A non-volatile memory having instructions stored thereon, wherein a processor, when executing the instructions, is used to implement a positioning method for fused lane lines as described in any one of claims 1 to 4.

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