Robust visual SLAM system in complex environment

Abstract

The invention relates to the technical field of synchronous positioning and map construction, in particular to a robust visual SLAM system in a complex environment, and aims to solve the problem that driving safety is affected due to the fact that a traditional visual SLAM system is often difficult to maintain high positioning precision and map construction quality and difficult to find obstacles in a driving route in the complex environment. The robust visual SLAM system comprises an environment monitoring module, an environment analysis module, a historical data module, a data analysis module, an enhanced display module and a map display module. According to the robust visual SLAM system, firstly, environmental factors are accurately judged, and a constructed map is matched according to information of historical data, so that conditions which are difficult to monitor in a complex environment are complementarily displayed, and high-precision positioning and map construction in the complex environment are realized; and adverse effects caused by a complex environment are eliminated and improved, so that the method has higher robustness.

Description

Technical Field

[0001] The present invention relates to the technical field of synchronous positioning and map construction, and in particular to a robust visual SLAM system in a complex environment. Background Art

[0002] With the rapid development of autonomous driving technology, visual SLAM systems have been widely used as a key technology for achieving precise positioning and map construction of autonomous driving vehicles. The patent with application number CN202310195225.6 discloses a visual SLAM system and method for autonomous driving vehicles, the system comprising an image preprocessing module, a multi-target tracking module, a visual odometer module, an object state update module and a back-end optimization module. The method comprises: step S1, preprocessing the image through an instance segmentation network; step S2, distinguishing the dynamic and static state of the object; step S3, estimating the initial camera pose; step S4, removing dynamic objects; step S5, performing an optimization strategy on the image keyframe. The beneficial effects of the invention are: in the visual image, efficient dynamic object filtering can be achieved, the ghosting of the map can be effectively reduced, the accuracy and robustness of positioning can be improved, and the applicability of the autonomous driving vehicle in a dynamic environment can be improved, but the following shortcomings still exist: in a complex environment, the traditional visual SLAM system is often difficult to maintain a high positioning accuracy and map construction quality, and it is difficult to find obstacles in the driving route, thereby affecting driving safety. Therefore, developing a robust visual SLAM system in complex environments is of great significance for improving driving safety.

[0003] The methods described in this section are not necessarily methods that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any method described in this section is considered to be prior art simply because it is included in this section. Similarly, unless otherwise indicated, the issues mentioned in this section should not be considered to have been recognized in any prior art. Summary of the invention

[0004] In order to overcome the above-mentioned technical problems, the purpose of the present invention is to provide a robust visual SLAM system in complex environments, which solves the problem that traditional visual SLAM systems often find it difficult to maintain high positioning accuracy and map construction quality in complex environments, and find it difficult to detect obstacles in the driving route, thereby affecting driving safety.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A robust visual SLAM system in a complex environment, comprising:

[0007] The environment monitoring module is used to monitor the current environment, obtain environment monitoring parameters, and send the environment monitoring parameters to the environment analysis module; wherein the environment monitoring parameters include light information GX, wind speed information FS, and dust information HC;

[0008] The environmental analysis module is used to obtain the environmental monitoring coefficient HJ according to the environmental monitoring parameters, and generate a normal display instruction or a historical data analysis instruction according to the environmental monitoring coefficient HJ, and send the normal display instruction to the map display module and send the historical data analysis instruction to the historical data module;

[0009] A map display module is used to directly use SLAM technology to construct and display a local map after receiving a normal display command;

[0010] A historical data module, configured to obtain an analysis route i according to a reference route and historical data after receiving a historical data analysis instruction, and send the analysis route i to a data analysis module;

[0011] A data analysis module is used to obtain data analysis parameters of the analysis route i, obtain a data analysis coefficient SF according to the data analysis parameters, obtain an enhanced route according to the data analysis coefficient SF, and send the enhanced route to the enhanced display module; wherein the data analysis parameters include time information SJ and an environmental monitoring coefficient HJ;

[0012] The specific process of the data analysis module obtaining the enhanced route is as follows:

[0013] The time information SJ and the environmental monitoring coefficient HJ are quantified, the values ​​of the time information SJ and the environmental monitoring coefficient HJ are extracted, and substituted into the formula for calculation. The data analysis coefficient SF is obtained, where ε is the preset error adjustment factor, ε=0.915, s1 and s2 are the preset weight factors corresponding to the set time information SJ and the environmental monitoring coefficient HJ, s1 and s2 satisfy s2>s1>1.159, s1=1.31, s2=1.76;

[0014] All analysis routes i are sorted in ascending order according to the data analysis coefficient SF, and the analysis route i at the top is marked as an enhanced route, and the enhanced route is sent to the enhanced display module;

[0015] The enhanced display module is used to obtain obstacle information according to the enhanced route and send the obstacle information to the map display module;

[0016] The map display module is used to construct a local map using SLAM technology, and to enhance the display of the local map according to obstacle information to form an enhanced map and display it.

[0017] As a further solution of the present invention: the specific process of the environment monitoring module acquiring the illumination information GX is as follows:

[0018] Get the light intensity in the current environment and mark it as the light intensity value, get the light intensity difference between the light intensity value and the preset standard light intensity, and mark it as the light value GZ, get the angle between the light direction and the image acquisition point, and mark it as the angle value, get the angle difference between the angle value and the preset standard angle, and mark it as the angle value JD, quantify the light value GZ and the angle value JD, extract the values ​​of the light value GZ and the angle value JD, and substitute them into the formula for calculation. According to the formula The illumination information GX is obtained, wherein g1 and g2 are preset proportional coefficients corresponding to the set illumination value GZ and angle value JD, respectively, and g1 and g2 satisfy g1+g2=1, 0<g2<g1<1, and g1=0.68 and g2=0.32.

[0019] As a further solution of the present invention: the specific process of the environmental monitoring module obtaining the wind speed information FS is as follows:

[0020] Get the wind speed in the current environment and mark it as wind speed information FS.

[0021] As a further solution of the present invention: the specific process of the environmental monitoring module obtaining the dust information HC is as follows:

[0022] The dust content in the air of the current environment is obtained and marked as dust information HC.

[0023] As a further solution of the present invention: the specific process of the environmental analysis module obtaining the environmental monitoring coefficient HJ is as follows:

[0024] The light information GX, wind speed information FS and dust information HC are quantified, the values ​​of the light information GX, wind speed information FS and dust information HC are extracted, and substituted into the formula for calculation. According to the formula The environmental monitoring coefficient HJ is obtained, where π is a mathematical constant, h1, h2 and h3 are the preset weight factors corresponding to the set light information GX, wind speed information FS and dust information HC, respectively, and h1, h2 and h3 satisfy h1>h3>h2>1.68, and h1=2.93, h2=1.85, and h3=2.21.

[0025] As a further solution of the present invention: the specific process of the environment analysis module generating the normal display instruction is as follows:

[0026] Compare the environmental monitoring coefficient HJ with the preset environmental monitoring threshold HJy:

[0027] If the environment monitoring coefficient HJ<the environment monitoring threshold value HJy, a normal display instruction is generated and sent to the map display module.

[0028] As a further solution of the present invention: the specific process of the environmental analysis module generating the historical data analysis instruction is as follows:

[0029] Compare the environmental monitoring coefficient HJ with the preset environmental monitoring threshold HJy:

[0030] If the environmental monitoring coefficient HJ≥the environmental monitoring threshold HJy, a historical data analysis instruction is generated and sent to the historical data module.

[0031] As a further solution of the present invention: the specific process of the historical data module obtaining the analysis route i is as follows:

[0032] Obtain the user's driving route and mark it as a reference route;

[0033] After receiving the historical data analysis instruction, all the travel routes of the same route as the reference route are obtained from the historical data, and marked as analysis route i, i = 1, ..., n, n is a positive integer, n represents the total number of analysis routes, and i is the number of any analysis route;

[0034] Send the analysis route i to the data analysis module.

[0035] As a further solution of the present invention: the specific process of the data analysis module obtaining the data analysis parameters is as follows:

[0036] Get the start time and current time of the analyzed route i, obtain the time difference between the two, and mark it as time information SJ;

[0037] The environmental monitoring coefficient HJ of the analysis route i is obtained and marked as environmental information HX.

[0038] As a further solution of the present invention: the specific process of the map display module obtaining the enhanced map is as follows:

[0039] The local map is constructed using SLAM technology, and an obstacle model is constructed based on the obstacle information. The obstacle model is overlapped on the constructed local map based on the location of the obstacle. An enhanced map is then formed and displayed. When the preset distance around the obstacle is reached, the obstacle information is used for voice broadcast prompts.

[0040] Beneficial effects of the present invention:

[0041] The present invention discloses a robust visual SLAM system in a complex environment. The system first collects and analyzes data on the environment to obtain environmental monitoring parameters. The environmental monitoring coefficient obtained according to the environmental monitoring parameters can comprehensively measure the complexity of the environment. The larger the environmental monitoring coefficient, the higher the complexity, and the higher the impact on synchronous positioning and map construction. When the environmental monitoring coefficient is small, the impact is low, and the map can be directly constructed. When the environmental monitoring coefficient is large, the impact is high, and the map cannot be directly constructed and displayed. First, historical data is collected and analyzed to obtain data analysis parameters. The data analysis coefficient obtained according to the data analysis parameters can measure the reliability of the analysis route. The smaller the data analysis coefficient, the higher the reliability of the analysis route. Obstacle information is obtained according to the enhanced route to enhance the display of the local map, form an enhanced map and display it, and can effectively display obstacles that are difficult to monitor in a complex environment, improve the reliability of the constructed map, and thus improve the driving safety of users.

[0042] The robust visual SLAM system of the present invention firstly makes an accurate judgment on environmental factors and matches the constructed map according to the information of historical data, thereby providing a supplementary display of situations that are difficult to monitor in complex environments, achieving high-precision positioning and map construction in complex environments, eliminating and improving the adverse effects caused by complex environments, and making it more robust. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a principle block diagram of a robust visual SLAM system in a complex environment in the present invention;

[0044] Figure 2 It is a flow chart of the working method of a robust visual SLAM system in a complex environment in the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0046] Embodiment 1:

[0047] See also Figure 1 As shown, this embodiment is a robust visual SLAM system in a complex environment, including the following modules: an environment monitoring module, an environment analysis module, a historical data module, a data analysis module, an enhanced display module and a map display module;

[0048] The environment monitoring module is used to monitor the current environment, obtain environment monitoring parameters, and send the environment monitoring parameters to the environment analysis module; wherein the environment monitoring parameters include light information GX, wind speed information FS and dust information HC;

[0049] The environmental analysis module is used to obtain the environmental monitoring coefficient HJ according to the environmental monitoring parameters, and generate a normal display instruction or a historical data analysis instruction according to the environmental monitoring coefficient HJ, and send the normal display instruction to the map display module, and send the historical data analysis instruction to the historical data module;

[0050] Wherein, the historical data module is used to obtain an analysis route i according to the reference route and historical data after receiving the historical data analysis instruction, and send the analysis route i to the data analysis module;

[0051] The data analysis module is used to obtain data analysis parameters of the analysis route i, obtain data analysis coefficient SF according to the data analysis parameters, obtain an enhanced route according to the data analysis coefficient SF, and send the enhanced route to the enhanced display module; wherein the data analysis parameters include time information SJ and environmental monitoring coefficient HJ;

[0052] Wherein, the enhanced display module is used to obtain obstacle information according to the enhanced route and send the obstacle information to the map display module;

[0053] Among them, the map display module is used to directly use SLAM technology to construct and display a local map after receiving a normal display instruction; it is also used to use SLAM technology to construct a local map, and enhance the display of the local map according to obstacle information to form an enhanced map and display it.

[0054] Embodiment 2:

[0055] See also Figure 2 As shown, this embodiment is a working method of a robust visual SLAM system in a complex environment, comprising the following steps:

[0056] Step s1: the environment monitoring module monitors the current environment, obtains environment monitoring parameters, wherein the environment monitoring parameters include light information GX, wind speed information FS and dust information HC, and sends the environment monitoring parameters to the environment analysis module;

[0057] Step s2: the environmental analysis module obtains the environmental monitoring coefficient HJ according to the environmental monitoring parameters, and generates a normal display instruction or a historical data analysis instruction according to the environmental monitoring coefficient HJ, and sends the normal display instruction to the map display module, and sends the historical data analysis instruction to the historical data module;

[0058] Step s3: After receiving the normal display instruction, the map display module directly uses the SLAM technology to construct and display the local map;

[0059] Step s4: after receiving the historical data analysis instruction, the historical data module obtains the analysis route i according to the reference route and the historical data, and sends the analysis route i to the data analysis module;

[0060] Step s5: the data analysis module obtains data analysis parameters of the analysis route i, wherein the data analysis parameters include time information SJ and environmental monitoring coefficient HJ, obtains data analysis coefficient SF according to the data analysis parameters, obtains an enhanced route according to the data analysis coefficient SF, and sends the enhanced route to the enhanced display module;

[0061] Step s6: the enhanced display module obtains obstacle information according to the enhanced route, and sends the obstacle information to the map display module;

[0062] Step s7: The map display module uses SLAM technology to construct a local map, and enhances the display of the local map according to the obstacle information to form an enhanced map and display it.

[0063] Embodiment 3:

[0064] Based on any of the above embodiments, Embodiment 3 of the present invention is an environment monitoring module. The function of the environment monitoring module is to obtain environment monitoring parameters, wherein the environment monitoring parameters include light information GX, wind speed information FS and dust information HC. The specific process is as follows:

[0065] The environmental monitoring module obtains the light intensity in the current environment and marks it as the light intensity value, obtains the light intensity difference between the light intensity value and the preset standard light intensity, and marks it as the light value GZ, obtains the angle between the light direction and the image acquisition point, and marks it as the angle value, obtains the angle difference between the angle value and the preset standard angle, and marks it as the angle value JD, quantifies the light value GZ and the angle value JD, extracts the values ​​of the light value GZ and the angle value JD, and substitutes them into the formula for calculation. According to the formula Obtain illumination information GX, wherein g1 and g2 are preset proportional coefficients corresponding to the set illumination value GZ and angle value JD, respectively, and g1 and g2 satisfy g1+g2=1, 0<g2<g1<1, and g1=0.68 and g2=0.32;

[0066] The environment monitoring module obtains the wind speed in the current environment and marks it as wind speed information FS;

[0067] The environment monitoring module obtains the dust content in the air in the current environment and marks it as dust information HC;

[0068] The environment monitoring module sends the illumination information GX, the wind speed information FS and the dust information HC to the environment analysis module.

[0069] Embodiment 4:

[0070] Based on any of the above embodiments, Embodiment 4 of the present invention is an environment analysis module. The function of the environment analysis module is to generate a normal display instruction or a historical data analysis instruction. The specific process is as follows:

[0071] The environmental analysis module quantifies the illumination information GX, wind speed information FS and dust information HC, extracts the values ​​of the illumination information GX, wind speed information FS and dust information HC, and substitutes them into the formula for calculation. The environmental monitoring coefficient HJ is obtained, where π is a mathematical constant, h1, h2 and h3 are respectively the preset weight factors corresponding to the set light information GX, wind speed information FS and dust information HC, h1, h2 and h3 satisfy h1>h3>h2>1.68, and h1=2.93, h2=1.85, h3=2.21;

[0072] The environmental analysis module compares the environmental monitoring coefficient HJ with the preset environmental monitoring threshold HJy:

[0073] If the environmental monitoring coefficient HJ is less than the environmental monitoring threshold HJy, a normal display instruction is generated and sent to the map display module;

[0074] If the environmental monitoring coefficient HJ≥the environmental monitoring threshold HJy, a historical data analysis instruction is generated and sent to the historical data module.

[0075] Embodiment 5:

[0076] Based on any of the above embodiments, Embodiment 5 of the present invention is a historical data module. The function of the historical data module is to obtain the analysis route i. The specific process is as follows:

[0077] The historical data module obtains the user's driving route and marks it as a reference route;

[0078] After receiving the historical data analysis instruction, the historical data module obtains the driving routes of all completed trips that are the same as the reference route from the historical data, and marks them as analysis routes i, where i=1, ..., n, where n is a positive integer, n represents the total number of analysis routes, and i is the number of any one of the analysis routes;

[0079] The historical data module sends the analysis route i to the data analysis module.

[0080] Embodiment 6:

[0081] Based on any of the above embodiments, Embodiment 6 of the present invention is a data analysis module. The function of the data analysis module is to obtain an enhanced route. The specific process is as follows:

[0082] The data analysis module obtains the start time and current time of the analysis route i, obtains the time difference between the two, and marks it as time information SJ;

[0083] The data analysis module obtains the environmental monitoring coefficient HJ of the analysis route i and marks it as environmental information HX;

[0084] The data analysis module quantifies the time information SJ and the environmental monitoring coefficient HJ, extracts the values ​​of the time information SJ and the environmental monitoring coefficient HJ, and substitutes them into the formula for calculation. The data analysis coefficient SF is obtained, where ε is the preset error adjustment factor, ε=0.915, s1 and s2 are the preset weight factors corresponding to the set time information SJ and the environmental monitoring coefficient HJ, s1 and s2 satisfy s2>s1>1.159, s1=1.31, s2=1.76;

[0085] The data analysis module sorts all analysis routes i according to the data analysis coefficient SF from small to large, marks the analysis route i at the first position as an enhanced route, and sends the enhanced route to the enhanced display module.

[0086] Embodiment 7:

[0087] Based on any of the above embodiments, Embodiment 7 of the present invention is an enhanced display module. The function of the enhanced display module is to obtain obstacle information. The specific process is as follows:

[0088] The enhanced display module obtains the driving video during the process of completing the enhanced route, obtains obstacles in the driving video, generates obstacle information according to the obstacles, and sends the obstacle information to the map display module; wherein the obstacle types include roadside parked vehicles, road construction areas, road snow or ice areas, road potholes, and road subsidence areas; the obstacle information includes the obstacle type and the location of the obstacle;

[0089] The enhanced display module sends the obstacle information to the map display module.

[0090] Embodiment 8:

[0091] Based on any of the above embodiments, Embodiment 8 of the present invention is a map display module. The function of the map display module is to display a map. The specific process is as follows:

[0092] After receiving the normal display command, the map display module directly uses SLAM technology to build and display the local map;

[0093] The map display module uses SLAM technology to construct a local map, and constructs an obstacle model based on the obstacle information. The obstacle model is overlapped on the constructed local map according to the location of the obstacle, and then an enhanced map is formed and displayed. When the preset distance around the obstacle location is reached, the obstacle information is used for voice broadcast prompts.

[0094] Based on Examples 1-8, the working principle of the present invention is as follows:

[0095] A robust visual SLAM system in a complex environment of the present invention monitors the current environment through an environmental monitoring module to obtain environmental monitoring parameters, wherein the environmental monitoring parameters include illumination information, wind speed information, and dust information; an environmental monitoring coefficient is obtained according to the environmental monitoring parameters through an environmental analysis module; a normal display instruction or a historical data analysis instruction is generated according to the environmental monitoring coefficient; after receiving the normal display instruction through a map display module, a local map is directly constructed and displayed using SLAM technology; after receiving the historical data analysis instruction through a historical data module, an analysis route is obtained according to a reference route and historical data; data analysis parameters of the analysis route are obtained through a data analysis module, wherein the data analysis parameters include time information and an environmental monitoring coefficient; a data analysis coefficient is obtained according to the data analysis parameters; an enhanced route is obtained according to the data analysis coefficient; obstacle information is obtained according to the enhanced route through an enhanced display module; a local map is constructed using SLAM technology through a map display module; and according to the obstacle information The local map is displayed and enhanced to form an enhanced map and display it; the system first collects and analyzes data on the environment to obtain environmental monitoring parameters. The environmental monitoring coefficient obtained based on the environmental monitoring parameters can comprehensively measure the complexity of the environment. The larger the environmental monitoring coefficient, the higher the complexity, and the higher the impact on synchronous positioning and map construction. When the environmental monitoring coefficient is small, it means that the impact is low and the map can be directly constructed. When the environmental monitoring coefficient is large, it means that the impact is high and the map cannot be directly constructed and displayed. First, historical data is collected and analyzed to obtain data analysis parameters. The data analysis coefficient obtained based on the data analysis parameters can measure the reliability of the analysis route. The smaller the data analysis coefficient, the higher the reliability of the analysis route. Obstacle information is obtained based on the enhanced route to enhance the display of the local map, form an enhanced map and display it. Obstacles that are difficult to monitor in complex environments can be effectively displayed, the reliability of the constructed map is improved, and the user's driving safety is improved.

[0096] The robust visual SLAM system of the present invention firstly makes an accurate judgment on environmental factors and matches the constructed map according to the information of historical data, thereby providing a supplementary display of situations that are difficult to monitor in complex environments, achieving high-precision positioning and map construction in complex environments, eliminating and improving the adverse effects caused by complex environments, and making it more robust.

[0097] It should be further explained that the above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0098] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0099] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

Claims

1. A robust visual SLAM system in a complex environment, characterized in that: include: The environment monitoring module is used to monitor the current environment, obtain environment monitoring parameters, and send the environment monitoring parameters to the environment analysis module; wherein the environment monitoring parameters include light information GX, wind speed information FS, and dust information HC; The environmental analysis module is used to obtain the environmental monitoring coefficient HJ according to the environmental monitoring parameters, and generate a normal display instruction or a historical data analysis instruction according to the environmental monitoring coefficient HJ, and send the normal display instruction to the map display module and send the historical data analysis instruction to the historical data module; A historical data module, configured to obtain an analysis route i according to a reference route and historical data after receiving a historical data analysis instruction, and send the analysis route i to a data analysis module; A data analysis module is used to obtain data analysis parameters of the analysis route i, obtain a data analysis coefficient SF according to the data analysis parameters, obtain an enhanced route according to the data analysis coefficient SF, and send the enhanced route to the enhanced display module; wherein the data analysis parameters include time information SJ and an environmental monitoring coefficient HJ; The specific process of the data analysis module obtaining the enhanced route is as follows: The time information SJ and the environmental monitoring coefficient HJ are quantified according to the formula The data analysis coefficient SF is obtained, where ε is the preset error adjustment factor, s1 and s2 are the preset weight factors corresponding to the set time information SJ and the environmental monitoring coefficient HJ respectively; All analysis routes i are sorted in ascending order according to the data analysis coefficient SF, and the analysis route i at the top is marked as an enhanced route, and the enhanced route is sent to the enhanced display module; The enhanced display module is used to obtain obstacle information according to the enhanced route and send the obstacle information to the map display module; The map display module is used to directly use the SLAM technology to build and display a local map after receiving a normal display instruction; it is also used to use the SLAM technology to build a local map, and enhance the display of the local map according to obstacle information to form an enhanced map and display it.

2. A robust visual SLAM system in a complex environment according to claim 1, characterized in that: The specific process of the environment monitoring module obtaining the illumination information GX is as follows: Get the light intensity in the current environment and mark it as the light intensity value, get the light intensity difference between the light intensity value and the preset standard light intensity, and mark it as the light value GZ, get the angle between the light direction and the image acquisition point, and mark it as the angle value, get the angle difference between the angle value and the preset standard angle, and mark it as the angle value JD, quantify the light value GZ and the angle value JD, according to the formula The illumination information GX is obtained, wherein g1 and g2 are preset proportional coefficients corresponding to the set illumination value GZ and angle value JD respectively.

3. The robust visual SLAM system in a complex environment according to claim 1, characterized in that: The specific process of the environmental monitoring module obtaining the wind speed information FS is as follows: Get the wind speed in the current environment and mark it as wind speed information FS.

4. The robust visual SLAM system in a complex environment according to claim 1, characterized in that: The specific process of the environmental monitoring module obtaining dust information HC is as follows: The dust content in the air of the current environment is obtained and marked as dust information HC.

5. The robust visual SLAM system in a complex environment according to claim 1, characterized in that: The specific process of the environmental analysis module obtaining the environmental monitoring coefficient HJ is as follows: The light information GX, wind speed information FS and dust information HC are quantified according to the formula The environmental monitoring coefficient HJ is obtained, wherein π is a mathematical constant, and h1, h2 and h3 are preset weight factors corresponding to the set illumination information GX, wind speed information FS and dust information HC respectively.

6. The robust visual SLAM system in a complex environment according to claim 1, characterized in that: The specific process of the environment analysis module generating a normal display instruction is as follows: Compare the environmental monitoring coefficient HJ with the preset environmental monitoring threshold HJy: If the environment monitoring coefficient HJ<the environment monitoring threshold value HJy, a normal display instruction is generated and sent to the map display module.

7. The robust visual SLAM system in a complex environment according to claim 1, characterized in that: The specific process of the environment analysis module generating historical data analysis instructions is as follows: Compare the environmental monitoring coefficient HJ with the preset environmental monitoring threshold HJy: If the environmental monitoring coefficient HJ≥the environmental monitoring threshold HJy, a historical data analysis instruction is generated and sent to the historical data module.

8. The robust visual SLAM system in a complex environment according to claim 1, characterized in that: The specific process of the historical data module obtaining the analysis route i is as follows: Obtain the user's driving route and mark it as a reference route; After receiving the historical data analysis instruction, the driving routes of all completed trips that are the same as the reference route are obtained from the historical data, and marked as analysis route i; Send the analysis route i to the data analysis module.

9. The robust visual SLAM system in a complex environment according to claim 1, characterized in that: The specific process of the data analysis module obtaining data analysis parameters is as follows: Get the start time and current time of the analyzed route i, obtain the time difference between the two, and mark it as time information SJ; The environmental monitoring coefficient HJ of the analysis route i is obtained and marked as environmental information HX.

10. The robust visual SLAM system in a complex environment according to claim 1, characterized in that: The specific process of the map display module obtaining the enhanced map is as follows: The local map is constructed using SLAM technology, and an obstacle model is constructed based on the obstacle information. The obstacle model is overlapped on the constructed local map based on the location of the obstacle. An enhanced map is then formed and displayed. When the preset distance around the obstacle is reached, the obstacle information is used for voice broadcast prompts.

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