CN107167148A - Synchronous positioning and map construction method and device - Google Patents

Abstract

The invention discloses a method and device for synchronous positioning and map construction. The synchronous positioning and map construction method includes the steps of: loading a preset robot kinematics model based on the structure of the robot; Establish a reference environment map; when moving to the next position, estimate the first estimated pose of the robot through the inertial navigation system; and obtain an updated environment map through sensor scanning, according to the update environment map, the reference environment map and obtaining a second estimated pose and a new reference environment map from the first estimated pose; obtaining an updated pose according to the first estimated pose and the second estimated pose; adjusting the pose of the robot according to the updated pose to update. The invention has the effect of improving the convenience and precision of synchronous positioning and map construction.

Description

Synchronous positioning and map construction method and device

technical field

The invention relates to the technical field of robots, in particular to a method and device for synchronous positioning and map construction.

Background technique

Simultaneous Localization and Mapping (SLAM for short), which means that when the robot is in an unknown place in advance, it incrementally builds the surrounding environment map by continuously moving in the environment, and simultaneously calculates its own pose in the environment. Because the position of the robot has errors in each step of operation, and the observed environmental feature information is highly related to it, fundamentally speaking, SLAM is an estimation problem, that is, in the case of noise interference, using the sensor carried by the robot itself The observed information is used to estimate the environment map and the trajectory of the robot in this environment map. At present, there are mainly technologies for positioning robots based on electromagnetic wires, infrared rays, lasers, and visual sensors.

However, the existing technology generally needs to set a reference object in the environment in advance, so there are disadvantages of low convenience and low precision.

Contents of the invention

The main purpose of the present invention is to provide a method and device for synchronous positioning and map construction, aiming at improving the convenience and accuracy of synchronous positioning and map construction.

In order to achieve the above object, a method for synchronous positioning and map construction proposed by the present invention is used for multi-robots, and the method for synchronous positioning and map construction includes steps:

Load the preset robot kinematics model based on the structure of the robot;

Create a reference environment map in a preset coordinate system by scanning the environment with sensors;

When moving to a next location, estimating a first estimated pose of the robot by an inertial navigation system; and,

Obtaining an updated environment map through sensor scanning, and obtaining a second estimated pose and a new reference environment map according to the updated environment map, the reference environment map, and the first estimated pose;

obtaining an updated pose according to the first estimated pose and the second estimated pose;

The pose of the robot is updated according to the updated pose.

Preferably, the establishment of a reference environment map in a preset coordinate system by scanning the environment with sensors includes:

Collect the two-dimensional point set of obstacle information in the field environment through sensor scanning;

The two-dimensional point set is converted into a grid map to establish a reference environment map in a preset coordinate system.

Preferably, when moving to the next position, estimating the first estimated pose of the robot through the inertial navigation system includes:

When moving to the next position, judge the moving distance of the robot according to the encoder;

According to the electronic compass and gyroscope to determine the direction of robot movement;

According to the movement distance and movement direction of the robot, the current pose of the robot is calculated and obtained, and the current pose of the robot is taken as the first estimated pose of the robot.

Preferably, said obtaining an updated environment map through sensor scanning, and obtaining a second estimated pose and a new reference environment map according to said updated environment map, reference environment map and first estimated pose include:

Obtain an updated environmental map through sensor scanning;

According to the first estimated pose, using an iterative closest point algorithm to map the updated environment map onto the reference environment map, and obtain a new reference environment map;

The second estimated pose is obtained according to the reference environment map, an updated environment map mapped to the reference environment map, and a robot space coordinate conversion algorithm.

Preferably, the method for synchronous positioning and map construction also includes:

Obtain reference road sign information through camera equipment;

When moving to the next location, obtain updated landmark information through camera equipment;

The Kalman gain matrix is obtained by calculating according to the reference landmark information and the updated landmark information, and the pose of the robot is updated according to the Kalman gain matrix.

The present invention also provides a device for synchronous positioning and map construction, the device for synchronous positioning and map construction includes a memory, a processor, and a program for synchronous positioning and map construction stored in the memory and operable on the processor , the following steps are implemented when the synchronous positioning and map building program is executed by the processor:

Load the preset robot kinematics model based on the structure of the robot;

Create a reference environment map in a preset coordinate system by scanning the environment with sensors;

When moving to a next location, estimating a first estimated pose of the robot by an inertial navigation system; and,

Obtaining an updated environment map through sensor scanning, and obtaining a second estimated pose and a new reference environment map according to the updated environment map, the reference environment map, and the first estimated pose;

obtaining an updated pose according to the first estimated pose and the second estimated pose;

The pose of the robot is updated according to the updated pose.

Preferably, the processor performing the step of scanning the environment with sensors to establish a reference environment map in a preset coordinate system includes:

Collect the two-dimensional point set of obstacle information in the field environment through sensor scanning;

The two-dimensional point set is converted into a grid map to establish a reference environment map in a preset coordinate system.

Preferably, the processor performing the step of estimating the first estimated pose of the robot through an inertial navigation system when moving to the next position includes:

When moving to the next position, judge the moving distance of the robot according to the encoder;

According to the electronic compass and gyroscope to determine the direction of robot movement;

According to the movement distance and movement direction of the robot, the current pose of the robot is calculated and obtained, and the current pose of the robot is taken as the first estimated pose of the robot.

Preferably, the processor executes the step of obtaining an updated environment map through sensor scanning, and obtaining a second estimated pose and a new reference environment map according to the updated environment map, the reference environment map, and the first estimated pose includes:

Obtain an updated environmental map through sensor scanning;

According to the first estimated pose, using an iterative closest point algorithm to map the updated environment map onto the reference environment map, and obtain a new reference environment map;

A second estimated pose is obtained according to the reference environment map, an updated environment map mapped to the reference environment map, and a robot space coordinate transformation algorithm.

Preferably, after the step of updating the pose of the robot according to the updated pose, the processor is further configured to execute the program of synchronous positioning and map construction, so as to realize the following steps:

Obtain reference road sign information through camera equipment;

When moving to the next location, obtain updated landmark information through camera equipment;

The Kalman gain matrix is obtained by calculating according to the reference landmark information and the updated landmark information, and the pose of the robot is updated according to the Kalman gain matrix.

The synchronous positioning and map construction method provided by the present invention can obtain the first estimated pose by using the inertial navigation system, and further use the sensor for scanning obstacles to continuously obtain the environmental map; The matching relationship between the front and rear environment maps is established, so that the map of the current environment can be quickly and accurately constructed; further, the second estimated pose is obtained through the matched front and rear environment maps, and then the first estimated pose and the second estimated pose are compared. Combined, to obtain a more accurate updated pose, so that the positioning accuracy of the robot is higher, and the cumulative error caused by inaccurate positioning is reduced, and then the current environment map with higher accuracy can be constructed synchronously.

Description of drawings

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 that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those skilled in the art, other drawings can also be obtained according to the structures shown in these drawings without creative effort.

Fig. 1 is the flowchart of the first embodiment of the synchronous positioning and map construction method of the present invention;

Fig. 2 is the flowchart of step S102 in Fig. 1;

Fig. 3 is the flowchart of step S103 in Fig. 1;

Fig. 4 is the flowchart of step S104 in Fig. 1;

Fig. 5 is a flow chart of the second embodiment of the synchronous positioning and map construction method of the present invention;

FIG. 6 is a schematic diagram of an embodiment of a device for synchronous positioning and map construction according to the present invention;

FIG. 7 is a schematic diagram of the workflow of the simultaneous positioning and map building device shown in FIG. 6 .

The realization of the purpose of the present invention, functional characteristics and advantages will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

detailed description

It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

Please refer to FIG. 1 , the first embodiment of the synchronous positioning and map construction method of the present invention, the synchronous positioning and map construction method is used for multi-robots, and the synchronous positioning and map construction method includes steps:

Step S101, loading a preset robot kinematics model established according to the structure of the robot.

Because the structure of the robot is different, there are cases where the motion structure of the large and small functions is different. Therefore, in order to target different robots, the kinematics model for each structure can be preset, so as to facilitate the accurate description of the motion pose of the robot. Specifically, the first kinematics model can include:

1. Determine the degree of freedom of the robot, such as a six-degree-of-freedom robot, that is, the pose of the robot can be determined by six variables based on the three-dimensional coordinates of the current reference position and the angles around three fixed axes. 2. According to the structure of the robot chassis, determine the relationship between the linear velocity and the angular velocity of the robot body. 3. Use the model to analyze the law of robot movement and posture, and use it as the mathematical basis for robot positioning.

In step S102, a reference environment map is established in a preset coordinate system by scanning the environment with sensors.

There can be many kinds of sensors, such as laser sensors, acoustic wave sensors, electromagnetic wave sensors (radar) and so on. These can determine obstacles in the environment by actively transmitting and receiving reflected waves.

Step S103, when moving to the next position, estimate the first estimated pose of the robot through the inertial navigation system.

The next position may be defined according to preset parameters, for example, when the wheel rotates 1 circle, moves 1 meter, or moves for 1 second, and so on. The inertial navigation system is based on Newton's laws of mechanics, by measuring the acceleration of the carrier in the inertial reference system, integrating it with respect to time, and transforming it into the navigation coordinate system, the speed, Information such as yaw angle and position. This embodiment can adopt, for example, a strapdown inertial navigation system, an analytical inertial navigation system, a semi-analytic inertial navigation system, and the like.

Step S104, obtaining an updated environment map through sensor scanning, and obtaining a second estimated pose and a new reference environment map according to the updated environment map, the reference environment map, and the first estimated pose.

The updated environment map is used to display the environment state of the robot's current location. The second estimated pose may be deduced by comparing changes in the updated environment map with the reference environment map.

Among them, matching only through two maps is less efficient and less accurate. Preferably, this embodiment uses the first estimated pose to determine the general change direction of the updated environment map relative to the reference environment map, and then matches the updated environment map and the reference environment map according to the general change direction, which can be faster And accurately determine the relationship between the updated environment map and the reference environment map. Then, the updated environment map and the reference environment map are combined to obtain a new reference environment map to achieve the function of real-time drawing of the map.

Further, it is also possible to perform a comparison calculation between the second estimated pose and the first estimated pose, thereby correcting the second estimated pose, and then obtain a more accurate new reference environment map based on the corrected second estimated pose.

Of course, the current estimated change map of the reference environment map can also be obtained by using the first estimated pose, and then the estimated change map is compared with the updated environment map, so as to determine the exact matching relationship between the reference environment map and the updated environment map. E.g:

With five trees ABCDE in the reference environment map, the robot moves forward. At the next position, the relative position relationship between the five trees ABCDE and the robot is confirmed through the first estimated pose, so as to obtain the current estimated change map. In this current estimate change map, the five trees ABCDE move backwards. At the same time, the robot obtains an updated environment map by scanning, and there are five trees CDEFG in the updated environment map. Then, by estimating the coordinate information of the tree CDE of the change map, confirming the matching relationship between the reference environment map and the tree CDE in the update environment map, and then mapping the update environment map to the reference environment map according to the matching result, so as to obtain a new reference environment map ; and when determined according to the matching relationship, the second estimated pose obtained by calculating the reference environment map and updating the environment map.

The above specific example is only used as an illustration of this step, and does not limit this step to only adopt the above solution.

Step S105, obtaining an updated pose according to the first estimated pose and the second estimated pose. Preferably, the updated pose can be obtained according to the first estimated pose and the second estimated pose, and the extended Kalman filter algorithm. Among them, the use of the extended Kalman filter algorithm will be described later.

Since both the first estimated pose and the second estimated pose are estimated values, it is not certain which estimated value is more accurate; in this embodiment, the more accurate one can be selected from the first estimated pose and the second estimated pose The precise one is used as the updated pose; or, an approximate estimation algorithm is used to extract a more accurate value from the first estimated pose and the second estimated pose to form an updated pose. For example, approximate estimation methods may include EKF, UT transform-based Kalman filter (UKF), particle filter, and the like.

Step S106, updating the pose of the robot according to the updated pose.

In this embodiment, the first estimated pose can be obtained by using the inertial navigation system, and the environmental map can be continuously obtained by further using the sensor for scanning obstacles; then the matching relationship between the front and rear environmental maps can be established through the guidance of the first estimated pose, so that A map of the current environment can be constructed quickly and accurately; further, the second estimated pose is obtained through the matched front and rear environment maps, and a more accurate update is obtained by combining the first estimated pose and the second estimated pose pose, so that the positioning accuracy of the robot is higher, and the cumulative error caused by inaccurate positioning is reduced, and then the current environment map with higher accuracy can be constructed synchronously.

Please refer to FIG. 2, step S102 in this embodiment, the establishment of a reference environment map in a preset coordinate system by scanning the environment with sensors includes:

In step S201, a two-dimensional point set of obstacle information of the on-site environment is collected through sensor scanning. Wherein, in this embodiment, the sensor adopts laser radar.

Step S202, converting the two-dimensional point set into a grid map to establish a reference environment map in a preset coordinate system. Wherein, the preset coordinate system may be a single coordinate system or a plurality of coordinate systems may be adopted at the same time; the coordinate system may be a polar coordinate system or a Cartesian coordinate system or the like.

In this embodiment, the two-dimensional point set information of obstacles is obtained by scanning, and converted into a grid map to construct a reference environment map, then this method has the effect of easy implementation and high stability. Of course, in other embodiments, it is also possible to collect more dimensional point sets, construct a vector map, and so on.

Please refer to FIG. 3, step S103 in this embodiment, when moving to the next position, estimating the first estimated pose of the robot through the inertial navigation system includes:

Step S301, when moving to the next position, judge the moving distance of the robot according to the encoder.

Step S302, judging the moving direction of the robot according to the electronic compass and gyroscope.

Step S303: Calculate and obtain the current pose of the robot according to the moving distance and moving direction of the robot, and use the current pose of the robot as a first estimated pose of the robot.

In this embodiment, the moving distance is obtained by using an encoder, and the moving direction is obtained by an electronic compass and a gyroscope. This inertial navigation system has the effects of being relatively common, low in cost, simple in structure and stable in operation.

Please refer to FIG. 4, step S104 in this embodiment, the updated environment map is obtained through sensor scanning, and the second estimated pose and new reference environment are obtained according to the updated environment map, the reference environment map, and the first estimated pose. Maps include:

Step S401, obtaining an updated environment map through sensor scanning. Among them, the sensor is the same as the previous sensor, specifically, it is a laser scanner carried by the robot body.

Step S402, according to the first estimated pose, use an iterative closest point algorithm to map the updated environment map onto the reference environment map, and obtain a new reference environment map.

In this step, the first estimated pose is used to guide the mapping of the updated environmental map to the reference environmental map, so that the matching relationship between the reference environmental map and the updated environmental map is obtained, and a new reference environmental map is obtained to construct a local environmental map Effect. Further adopting an iterative algorithm can determine the mapping relationship relatively simply and accurately. The iterative algorithm is described later.

Step S403: Obtain a second estimated pose according to the reference environment map, the updated environment map mapped to the reference environment map, and the robot space coordinate transformation algorithm.

In this embodiment, the first estimated pose is used to guide the matching between the updated environment and the reference environment map, and the second estimated pose is obtained according to the reference environment map, the updated environment map and the left transformation algorithm of robotics space, then It has the effect of reducing the calculation difficulty during matching and achieving fast and accurate calculation of the matching relationship; and the second estimated pose obtained through the matched front and rear environment maps has the effect of relatively accurate estimation.

Specifically, please refer to Figure 7, here is an explanation of the above formula: the extended Kalman filter: Kalman filter is a series of mathematical formulas proposed by Kalman himself in 1960, which can be obtained by observing the input and output of the system As a result, the optimal estimation of the state of the system including noise and disturbance is performed by iteratively solving the state information of the current moment using the data of the previous moment and the error information of the system. The premise of the application of the Kalman filter is based on the linear system, but it is difficult to ensure that the linear equation can accurately describe the state of the system in practical applications. Both the process model and the observation model of the system may exist in a nonlinear form, and the nonlinear model is shown by the following equation, where f and h represent nonlinear functions.

x(k)=f(x(k-1),u(k))+w(k)

z(k)=h(x(k))+v(k)

In practical applications, we usually cannot obtain the noise value in the nonlinear model, and noise will be introduced in the process of linearizing the system, so it can be assumed that the value of this stage is zero, and the disturbance is uniformly considered in the linearized system model. The result of linearizing the above nonlinear system is as follows:

The left side of the equal sign is the actual system state and observed variables, and the unmarked x(k) and z(k) on the right side of the equal sign are the corresponding estimated values. The noise disturbances of the w(k) and v(k) systems, and F(k) and H(k) are the Jacobian matrices associated with state transitions and observations.

The specific algorithm process is:

(1) According to the kinematics model of the robot, calculate the position change (Δx _t , Δy _t ) from time t-1 to time t and the yaw angle change θ _t of the IMU, and calculate the robot corresponding to the odometer at time t Estimated pose P _odom (t)=[x(t), y(t), θ(t)] ^T .

(2) Let q ₀ =(Δx _t ,Δy _t ,θ _t ), according to the scanning data S t of the laser radar at time _t and the reference scanning data S _{t-1 at time t-1} , q ₀ is taken as the starting point from S _{t- 1} to the initial pose transformation of S _t , execute the PLICP algorithm, and iteratively calculate the final pose transformation q _k . Then calculate the robot pose P _scan (t)=R(θ _k )P _scan (t-1)+t _k corresponding to the radar scan matching at time t.

Please refer to FIG. 5 , this embodiment is based on the first embodiment, and additional steps are added. details as follows:

Step S501 is the same as step S101 in the first embodiment, and will not be repeated here.

Step S502 is the same as step S102 in the first embodiment, and will not be repeated here.

Step S503 is the same as step S103 in the first embodiment, and will not be repeated here.

Step S504 is the same as step S104 in the first embodiment, and will not be repeated here.

Step S505 is the same as step S105 in the first embodiment, and will not be repeated here.

Step S506 is the same as step S106 in the first embodiment, and will not be repeated here.

In step S507, reference landmark information is obtained through the camera device.

Step S508, when moving to the next location, obtain updated landmark information through the camera device.

Step S509, calculating and obtaining a Kalman gain matrix according to the reference landmark information and updated landmark information, and updating the pose of the robot according to the Kalman gain matrix.

In this embodiment, by adding reference landmark features, the pose of the robot can be further calibrated to reduce positioning errors, thereby further improving the overall accuracy of simultaneous positioning and map construction.

Please refer to FIG. 6 , an embodiment of the device for synchronous positioning and map construction according to the present invention. The simultaneous positioning and map construction equipment is a four-wheel drive robot as a whole, including:

The laser radar 1001 is used to emit laser light and receive reflected laser light. In other embodiments, acoustic wave or light wave sensors can also be used.

The laser navigation module 1002 is used to calculate the information received by the laser radar, and then locate obstacles.

The electronic compass 1008 is used to obtain the current orientation of the robot.

The angle sensor 1009 is used to acquire the current rotation angle of the robot.

The encoder 1004 is used to record the moving distance of the robot.

The data acquisition board 1003 is used to connect the above-mentioned measurement modules and the control board.

The control panel 1005 is used for configuring and synchronously positioning and constructing a map according to the above-mentioned measured values.

The rear wheel DC brushless electric driver 1007 is used to drive the robot to move.

The front wheel stepper motor driver 1006 is used to drive the robot to move.

Please refer to Fig. 7 in conjunction with, the described synchronous positioning and map construction device also includes a memory arranged on the control board 1005, a processor and a synchronous positioning and map construction program stored on the memory and operable on the processor .

When the program for synchronous positioning and map construction is executed by the processor, the steps of the method for synchronous positioning and map construction in the above-mentioned embodiments are implemented. For a specific solution, reference may be made to the above embodiments of the method for synchronous positioning and map construction, which will not be repeated here.

It should be noted that, in this document, the term "comprising", "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element.

The serial numbers of the above embodiments of the present invention are for description only, and do not represent the advantages and disadvantages of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation. Based on such an understanding, the essence of the technical solution of the present invention or the part that contributes to the prior art can be embodied in the form of software products, and the computer software products are stored in a storage medium (such as ROM/RAM, disk, CD) contains several instructions to make a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in various embodiments of the present invention.

Embodiments of the present invention have been described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementations, and the above-mentioned specific implementations are only illustrative, rather than restrictive, and those of ordinary skill in the art will Under the enlightenment of the present invention, many forms can also be made without departing from the gist of the present invention and the protection scope of the claims, and these all belong to the protection of the present invention.

Claims (10)

1. a synchronous location and map construction method, for multi-robots, is characterized in that, described synchronous location and map construction method comprise steps: Load the preset robot kinematics model based on the structure of the robot; Create a reference environment map in a preset coordinate system by scanning the environment with sensors; When moving to a next location, estimating a first estimated pose of the robot by an inertial navigation system; and, Obtaining an updated environment map through sensor scanning, and obtaining a second estimated pose and a new reference environment map according to the updated environment map, the reference environment map, and the first estimated pose; obtaining an updated pose according to the first estimated pose and the second estimated pose; The pose of the robot is updated according to the updated pose. 2. The synchronous positioning and map construction method according to claim 1, wherein said scanning the environment by sensors and establishing a reference environment map in a preset coordinate system comprises: Collect the two-dimensional point set of obstacle information in the field environment through sensor scanning; The two-dimensional point set is converted into a grid map to establish a reference environment map in a preset coordinate system. 3. The synchronous positioning and map construction method according to claim 2, wherein, when moving to the next position, estimating the first estimated pose of the robot by an inertial navigation system comprises: When moving to the next position, judge the moving distance of the robot according to the encoder; According to the electronic compass and gyroscope to determine the direction of robot movement; According to the movement distance and movement direction of the robot, the current pose of the robot is calculated and obtained, and the current pose of the robot is taken as the first estimated pose of the robot. 4. The synchronous positioning and map construction method according to claim 3, wherein the updated environment map is obtained through sensor scanning, and the second estimated pose is obtained according to the updated environment map, the reference environment map and the first estimated pose. The pose and new reference environment maps include: Obtain an updated environmental map through sensor scanning; According to the first estimated pose, using an iterative closest point algorithm to map the updated environment map onto the reference environment map, and obtain a new reference environment map; The second estimated pose is obtained according to the reference environment map, an updated environment map mapped to the reference environment map, and a robot space coordinate transformation algorithm. 5. The synchronous positioning and map construction method according to claim 1, wherein the synchronous positioning and map construction method further comprises: Obtain reference road sign information through camera equipment; When moving to the next location, obtain updated landmark information through camera equipment; The Kalman gain matrix is obtained by calculating according to the reference landmark information and the updated landmark information, and the pose of the robot is updated according to the Kalman gain matrix. 6. A device for synchronous positioning and map construction, characterized in that the device for synchronous positioning and map construction includes a memory, a processor, and a synchronous positioning and map construction device stored on the memory and operable on the processor program, the following steps are implemented when the synchronous positioning and map building program is executed by the processor: Load the preset robot kinematics model based on the structure of the robot; Create a reference environment map in a preset coordinate system by scanning the environment with sensors; When moving to a next location, estimating a first estimated pose of the robot by an inertial navigation system; and, Obtaining an updated environment map through sensor scanning, and obtaining a second estimated pose and a new reference environment map according to the updated environment map, the reference environment map, and the first estimated pose; obtaining an updated pose according to the first estimated pose and the second estimated pose; The pose of the robot is updated according to the updated pose. 7. The synchronous positioning and map construction device according to claim 6, wherein the processor executes the step of scanning the environment with sensors and establishing a reference environment map in a preset coordinate system comprising: Collect the two-dimensional point set of obstacle information in the field environment through sensor scanning; The two-dimensional point set is converted into a grid map to establish a reference environment map in a preset coordinate system. 8. The simultaneous positioning and map construction device according to claim 7, wherein the processor executes the step of estimating the first estimated pose of the robot through an inertial navigation system when moving to the next position. include: When moving to the next position, judge the moving distance of the robot according to the encoder; According to the electronic compass and gyroscope to determine the direction of robot movement; According to the moving distance and moving direction of the robot, the current pose of the robot is calculated and obtained as the first estimated pose of the robot. 9. The device for synchronous positioning and map construction according to claim 8, wherein the processor executes the step of obtaining an updated environmental map through sensor scanning, and according to the updated environmental map, the reference environmental map, and the first estimated position pose to obtain a second estimated pose and a new reference environment map including: Obtain an updated environmental map through sensor scanning; According to the first estimated pose, using an iterative closest point algorithm to map the updated environment map onto the reference environment map, and obtain a new reference environment map; The second estimated pose is obtained according to the reference environment map, an updated environment map mapped to the reference environment map, and a robot space coordinate transformation algorithm. 10. The synchronous positioning and map construction device according to claim 6, wherein after the step of updating the pose of the robot according to the updated pose, the processor is further configured to execute The synchronous positioning and map construction program to achieve the following steps: Obtain reference road sign information through camera equipment; When moving to the next location, obtain updated landmark information through camera equipment; The Kalman gain matrix is obtained by calculating according to the reference landmark information and the updated landmark information, and the pose of the robot is updated according to the Kalman gain matrix.