IMU-odometer fusion positioning method, system and equipment and medium

Abstract

The invention discloses an IMU-odometer fusion positioning method which comprises the following steps: S1, acquiring angular velocity data and acceleration data of vehicle body movement through an IMU, and acquiring rotating speed information of wheels through an odometer; s2, inertial solution IMU collection data is carried out to obtain speed information, and multi-stage anomaly detection is carried out in combination with speedometer collection data to identify and obtain a data anomaly result; s3, inputting a data exception result to perform fuzzy reasoning so as to dynamically adjust and obtain the weights of the IMU acquisition data and the odometer acquisition data in the fusion process, and performing weighted summation according to the weights to obtain speed information after fusion processing; and S4, performing Kalman filtering fusion on the angular velocity data acquired by the IMU and the velocity information after fusion processing, and then outputting an accurate positioning result. According to the invention, real-time detection and processing of abnormal conditions such as wheel slip can be realized, and the result positioning precision is high.

Description

Technical Field

[0001] The present invention relates to the technical field of navigation and mobile positioning, and particularly relates to an IMU-odometer fusion positioning method, system, device and storage medium based on an adaptive sliding window and fuzzy logic. Background Art

[0002] In the fields of mobile robots and autonomous navigation vehicles, precise positioning is the key to achieving efficient navigation and control. Currently, the commonly used positioning methods mainly include inertial navigation based on an inertial measurement unit (IMU) and dead reckoning based on an odometer. These two methods have their own advantages and disadvantages:

[0003] IMU positioning has high accuracy in the short term and can provide high-frequency attitude and acceleration information. However, there is cumulative error, and long-term use will cause positioning drift;

[0004] Odometer positioning can directly measure the movement of the wheels and provide relatively accurate displacement information. However, it is easily affected by factors such as wheel slip and ground unevenness, and large errors will occur in some cases.

[0005] Although in the prior art, methods such as Kalman filtering are further used to fuse IMU and odometer data to try to overcome the above defects, these methods are still limited in dealing with abnormal situations such as severe wheel slip, resulting in a decrease in positioning accuracy.

[0006] Therefore, this application specifically proposes an IMU-odometer fusion positioning method to solve the above technical problems. Summary of the Invention

[0007] The main object of the present invention is to provide an IMU-odometer fusion positioning method to solve the technical problems proposed in the background art.

[0008] The present invention adopts the following technical solutions to solve the above technical problems:

[0009] An IMU-odometer fusion positioning method includes the following steps:

[0010] S1. Collect the angular velocity data and acceleration data of the vehicle body movement through the IMU, and collect the rotational speed information of the wheels through the odometer;

[0011] S2. Inertially solve the data collected by the IMU to obtain speed information, and perform multi-level anomaly detection in combination with the data collected by the odometer to identify and obtain the data anomaly result;

[0012] S3. Input the data anomaly result for fuzzy reasoning to dynamically adjust the weights of the data collected by the IMU and the data collected by the odometer in the fusion process, and obtain the speed information after fusion processing by weighted summation according to the weights;

[0013] S4. The angular velocity data collected by the IMU and the velocity information after fusion processing are fused through Kalman filtering to output accurate positioning results.

[0014] Preferably, the specific operation process of multi-level anomaly detection in the step S2 includes:

[0015] S21. Preset the thresholds of the motion state and the sampling points of the sliding window. Dynamically adjust the size of the sliding window according to the velocity information, the data collected by the odometer and the preset motion state thresholds, and update the data within the window;

[0016] S22. Perform multi-modal anomaly detection based on the sliding window data;

[0017] S23. According to the multi-modal anomaly detection results, assign preset weights to different result features according to the preset importance;

[0018] S24. According to the assigned weights, perform weighted summation on the multi-modal anomaly detection result features to obtain the overall anomaly detection score.

[0019] Preferably, the multi-modal anomaly detection in the step S22 includes:

[0020] (1) Compare the velocity characteristics within the sliding window and identify abnormal fluctuations based on the time-domain change;

[0021] (2) Perform fast Fourier transform on the data within the sliding window, extract the main frequency components and their amplitudes, and detect abnormal frequency patterns;

[0022] (3) Perform matching based on the template of historical data, and use the support vector machine (SVM) model to output the anomaly probability.

[0023] Preferably, the specific operation process of fuzzy inference in the step S3 includes:

[0024] S31. Input the data anomaly result, and set the membership function of the corresponding data according to the preset data interval for fuzzy processing;

[0025] S32. According to the data anomaly result after fuzzy processing, establish a fuzzy rule base including a normal working condition rule group and an abnormal working condition rule group;

[0026] S33. Use the Mandani fuzzy inference method for the data anomaly result, and then use the centroid method for defuzzification to ensure the continuity and differentiability of the weight output. Among them, the Mandani fuzzy inference method processes the AND relationship in the fuzzy rule base through the min operator, and processes the OR relationship in the fuzzy rule base through the max operator;

[0027] S34. Update the IMU weight according to the data result after defuzzification, and calculate the odometer weight at the same time, where the odometer weight is obtained through the following calculation formula: odometer weight = 1 - IMU weight, and the odometer weight is within a specified range.

[0028] Preferably, the specific input variables for defuzzification in the step S31 include:

[0029] The degree of speed difference, which represents the difference between the estimated speeds of the imu and the odometer, and reflects the consistency of sensor data;

[0030] The anomaly detection result, which is used to indicate whether there are anomaly problems such as sensor failures and wheel slips in the current system state;

[0031] The historical weight information, which is used to represent the change trend of the IMU weight in the past period of time and is used to smooth the weight adjustment.

[0032] Preferably, the specific operation process of Kalman filter fusion in the step S4 includes:

[0033] S41. Define the moving state vectors including the pose, attitude, and speed of the vehicle body;

[0034] S42. Predict the next state of the vehicle body based on the current moving state vector of the vehicle body using the Newtonian kinematic model;

[0035] S43. Based on the angular velocity data collected by the IMU and the fused speed information, substitute the predicted value of the next state of the vehicle body into the Kalman filter, and then the accurate positioning result can be obtained and output.

[0036] Preferably, the present invention also discloses an IMU-odometer fusion positioning system for performing the IMU-odometer fusion positioning method described in any one of the above, including:

[0037] A sensor module, which consists of an IMU and an odometer;

[0038] An adaptive sliding window anomaly detection module, which is used to perform multi-level anomaly detection operations to identify and obtain data anomaly results;

[0039] A fuzzy logic weight adjustment module, which is used to perform fuzzy inference operations on the data anomaly results;

[0040] A Kalman filter module, which is used to fuse multi-source data and output a positioning result.

[0041] On the other hand, the present invention also discloses a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the processor is caused to execute the steps of the above method.

[0042] On the other hand, the present invention also discloses a computer device, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to execute the steps of the above method.

[0043] As can be seen from the above technical solutions, the present invention provides an IMU-odometer fusion positioning method. Compared with the prior art, the present invention has the following advantages:

[0044] 1. During the multi-level anomaly detection process of the present invention, by adopting an adaptive sliding window mechanism, the data processing window is dynamically adjusted, and the window size is optimized in real time according to the change of the motion state, so as to improve the accuracy and real-time performance of data capture, and realize the real-time detection and processing of abnormal situations such as wheel slippage.

[0045] 2. During the multi-level anomaly detection process of the present invention, it is also possible to monitor and identify abnormal situations in the IMU and odometer data in real time, such as sensor failures or noise interference, thereby further enhancing the robustness and reliability of the system.

[0046] 3. During the fuzzy reasoning process of the present invention, by using a fuzzy logic controller, the weights of the IMU and odometer data in the fusion process are dynamically adjusted by fuzzy logic according to the anomaly detection results, which can effectively combine the data advantages of the IMU and the odometer, realize smooth and intelligent weight allocation, and improve the positioning accuracy.

[0047] 4. The method of the present invention adopts a lightweight algorithm, which is applicable to various types of mobile robots and autonomous navigation vehicles, is convenient to be implemented and run in an embedded system, and has high computing efficiency and strong versatility at the same time.

[0048] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Of course, any product implementing the present invention does not necessarily need to achieve all the above advantages at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings forming a part of this application are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0050] Figure 1 is the overall flowchart of the method of the present invention;

[0051] Figure 2 is the schematic flowchart of the adaptive sliding window anomaly detection of the present invention;

[0052] Figure 3 Schematic diagram of the fuzzy inference process of the present invention. Specific embodiments

[0053] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0054] In the embodiment, refer in detail to Figures 1 to 3 .

[0055] An IMU-odometer fusion positioning method based on an adaptive sliding window and fuzzy logic is proposed in the embodiment of the present invention, which is implemented based on an IMU-odometer fusion positioning system. The positioning system is composed of multiple modules, namely a sensor module, an adaptive sliding window anomaly detection module, a fuzzy logic weight adjustment module, and a Kalman filter module, where:

[0056] In the sensor module, the IMU provides angular velocity and acceleration data, and the odometer provides the rotational speed information of the wheels;

[0057] The adaptive sliding window anomaly detection module obtains velocity information through inertial solution of IMU data, and performs multi-level anomaly detection in combination with odometer data to identify data anomaly situations;

[0058] The fuzzy logic weight adjustment module dynamically adjusts the weights of IMU and odometer data during the fusion process according to the results of fuzzy inference, and obtains the fused velocity information by weighted summation according to the weights;

[0059] The Kalman filter module performs the final data fusion and outputs accurate positioning results after processing multi-source data.

[0060] Specifically, as Figure 1 shown, the specific steps for the positioning system to execute the positioning method include:

[0061] (1) In the adaptive sliding window anomaly detection module, as Figure 2 shown, the execution method process is:

[0062] (11) Parameter initialization

[0063] Set the relevant parameters of the sliding window, including the threshold of the motion state and the size of the sliding window (such as 50 sampling points for the small window and 150 sampling points for the large window);

[0064] (12) Window Size Determination and Data Update

[0065] Dynamically adjust the size of the sliding window according to sensor data and preset motion state thresholds. By introducing multi-level thresholds, a smooth transition mechanism, and an adaptive adjustment strategy, more intelligent and flexible window size control is achieved, and the data within the window is updated to ensure the timeliness of anomaly detection. It is particularly suitable for rapidly changing motion states and can flexibly handle data characteristics in different scenarios.

[0066] Specifically, for window adjustment in low-speed or stationary states, the adjustment strategy adopted is as follows:

[0067] When the vehicle is in a low-speed or stationary state (speed < preset speed threshold), a smaller sliding window (such as 50 sampling points) is used to capture detailed changes in the data, especially abnormal fluctuations that may occur in complex environments, thereby enhancing the system's ability to capture details in low-speed scenarios, avoiding smoothing out important information due to an overly large window, and improving the accuracy of anomaly detection.

[0068] In addition, for window adjustment in high-speed motion states, the adjustment strategy adopted is as follows:

[0069] When the vehicle is in a high-speed motion state (speed > preset speed threshold), a larger sliding window (such as 150 sampling points) is used to smooth the noise and improve data stability.

[0070] (13) Multimodal Anomaly Detection

[0071] (a) Time-domain analysis: Compare statistical characteristics such as the mean and variance of the speed within the sliding window to identify abnormal fluctuations.

[0072] (b) Frequency-domain analysis: Perform a fast Fourier transform (FFT) on the data within the sliding window to extract the main frequency components and their amplitudes, and detect abnormal frequency patterns.

[0073] (c) Pattern matching: Match based on templates of historical data, and use a support vector machine (SVM) model to output the anomaly probability.

[0074] (14) Feature Weighted Comprehensive Scoring

[0075] Assign different weights according to the importance of various features, perform weighted summation on different features, and obtain the overall anomaly detection score.

[0076] In summary, during the multi-level anomaly detection process, by adopting an adaptive sliding window mechanism, the data processing window is dynamically adjusted, and the window size is optimized in real time according to the changes in the motion state, improving the accuracy and real-time performance of data capture, realizing the real-time detection and processing of anomalies such as wheel skidding. In addition, it can also monitor and identify anomalies in IMU and odometer data in real time, such as sensor failures or noise interference, thus further enhancing the robustness and reliability of the system.

[0077] Therefore, the adaptive sliding window mechanism adopted by this method is applicable to dynamic environments that require real-time monitoring and processing of sensor data anomalies, such as autonomous driving vehicles or mobile robots. Especially when the vehicle motion state changes (such as sudden acceleration, sudden braking, or wheel skidding), the window size is dynamically adjusted to optimize the accuracy and real-time performance of data capture.

[0078] (2) In the fuzzy logic weight adjustment module, as Figure 3 shown, the execution method process is as follows:

[0079] (21) Input variables and fuzzy processing

[0080] (a) The input variable is the degree of speed difference (V), which represents the degree of difference between the estimated speeds of the IMU and the odometer, reflecting the consistency of sensor data;

[0081] At this time, the linguistic variables are defined as: {very small, small, medium, large, very large};

[0082] The following membership function designs are available:

[0083] 1 Very small: [0, 0.2] m / s Trapezoidal membership function 2 Small: [0.1, 0.4] m / s Triangular membership function 3 Medium: [0.3, 0.7] m / s Triangular membership function 4 Large: [0.6, 0.9] m / s Triangular membership function 5 Very large: [0.8, +∞] m / s Trapezoidal membership function

[0084] In summary, further, for the specific description of the membership function, taking the membership function of the degree of speed difference as an example, there are:

[0085] (a1) Very small (Trapezoidal): A trapezoidal membership function defined on [0, 0.2], used to represent a "very small" speed difference:

[0086]

[0087] (a2) Small (Triangular): A triangular membership function defined on [0.1, 0.4], used to represent a "small" speed difference:

[0088]

[0089] (a3) Medium (Triangular): A triangular membership function defined on [0.3, 0.7], used to represent a "medium" speed difference:

[0090]

[0091] (a4) Large (Triangular): A triangular membership function defined on [0.6, 0.9], used to represent the "large" speed difference:

[0092]

[0093] (a5) Very Large (Trapezoidal): A trapezoidal membership function defined on [0.8, +∞), used to represent the "very large" speed difference:

[0094]

[0095] (b) The input variable is the anomaly detection result (A), indicating whether there is an anomaly in the current system state (such as sensor failure, wheel slip, etc.);

[0096] At this time, the linguistic variables are defined as: {Normal, Slight Anomaly, Moderate Anomaly, Severe Anomaly};

[0097] The following membership function designs are available:

[0098] 1 Normal: [0, 0.3] Trapezoidal membership function 2 Slight: [0.2, 0.5] Triangular membership function 3 Moderate: [0.4, 0.8] Triangular membership function 4 Severe: [0.7, 1.0] Trapezoidal membership function

[0099] Furthermore, for the specific description of the membership function, taking the membership function of the anomaly detection result as an example, there are:

[0100] (b1) Normal (Trapezoidal): A trapezoidal membership function defined on [0, 0.3], used to represent the "normal" anomaly detection result:

[0101]

[0102] (b2) Slight (Triangular): A triangular membership function defined on [0.2, 0.5], used to represent the "slight" anomaly detection result:

[0103]

[0104] (b3) Moderate (Triangular): A triangular membership function defined on [0.4, 0.8], used to represent the "moderate" anomaly detection result:

[0105]

[0106] (b4) Severe (Trapezoidal): A trapezoidal membership function defined on [0.7, 1.0], used to represent the "severe" anomaly detection result:

[0107]

[0108] (c) The input variable is historical weight information (H), which represents the changing trend of the IMU weight over a past period of time and is used to smooth the weight adjustment;

[0109] At this time, the linguistic variables are defined as: {basically stable, slightly rising, slightly falling, rising significantly, falling significantly};

[0110] There is the following membership function design:

[0111] 1 Basically stable: [-0.1, 0.1] Triangular membership function 2 Slight increase: [0, 0.25] Triangular membership function 3 Slight decrease: [-0.25, 0] Triangular membership function 4 Large increase: [0.2, 0.6] Triangular membership function 5 Large decrease [-0.6, -0.2] Triangular membership function

[0112] Furthermore, for the specific description of the membership function, taking the membership function of the historical weight information as an example, there is:

[0113] (c1) Basically stable (Triangular): A triangular membership function defined on [-0.1, 0.1] is used to represent the historical weight of "basically stable":

[0114]

[0115] (c2) Slightly rising (Triangular): A triangular membership function defined on [0, 0.25] is used to represent the historical weight of "slightly rising":

[0116]

[0117] (c3) Slightly falling (Triangular): A triangular membership function defined on [-0.25, 0] is used to represent the historical weight of "slightly falling":

[0118]

[0119] (c4) Rising significantly (Triangular): A triangular membership function defined on [0.2, 0.6] is used to represent the historical weight of "rising significantly":

[0120]

[0121] (c5) Falling significantly (Triangular): A triangular membership function defined on [-0.6, -0.2] is used to represent the historical weight of "falling significantly":

[0122]

[0123] (22) In the fuzzy logic weight adjustment module, a fuzzy rule base is set as follows:

[0124] (d) Normal operation rule group

[0125] Rule number Condition Conclusion Rule 1 IF V is very small AND A is normal AND H is basically stable IMU weight = low Rule 2 FV is small AND A is normal AND H is slight decrease IMU weight = medium - low Rule 3 IF V is medium AND A is normal AND H is slight increase IMU weight = medium Rule 4 IF V is very small AND A is normal AND H is slight increase IMU weight = medium - low Rule 5 FV is small AND A is normal AND H is large increase IMU weight = medium

[0126] (e) Abnormal operation rule group

[0127]

[0128]

[0129] In summary, during the fuzzy inference process of this method, by using a fuzzy logic controller, according to the anomaly detection results, the weights of IMU and odometer data in the fusion process are dynamically adjusted by fuzzy logic intelligence, which can adapt to the changes in data quality, effectively combine the data advantages of IMU and odometer, achieve smooth and intelligent weight allocation, and improve the positioning accuracy. At this time, fuzzy logic inference can also quickly process the anomaly results, ensuring the seamless connection between weight adjustment and data fusion.

[0130] Therefore, the fuzzy logic weight adjustment mechanism adopted by this method is applicable to complex environments where sensor data may be inconsistent or abnormal, such as uneven ground, sensor failures, or noise interference. By dynamically adjusting the weights of IMU and odometer data in the fusion process through fuzzy inference, the positioning accuracy and robustness can be improved.

[0131] (3) In the Kalman filter module, the execution method process is as follows:

[0132] (31) State vector definition

[0133] Define common state variables of the vehicle body including a 2D mobile robot, such as pose, attitude, speed, etc.;

[0134] (32) Prediction equation

[0135] Based on the basic Newtonian kinematic model, use the current moving state vector of the vehicle body to predict the next state of the vehicle body;

[0136] (33) Observation equation

[0137] Among them, the angular velocity is directly provided by the IMU observation, and the speed is provided by the weighted sum of the IMU and the odometer after fuzzy logic weighting. Based on the angular velocity data collected by the IMU and the speed information after fusion processing, substituting the predicted value of the next state of the vehicle body into the Kalman filter, the accurate positioning result can be obtained and output;

[0138] At this time, based on the observation space k, the observation equation in the Kalman filter fusion is:

[0139] z k =H k xk +v k

[0140] Wherein: is the observation vector, used to represent the IMU angular velocity information w in practice k and the velocity information v after fusion processing fusion ; x k = [p, v, θ] T is the state vector, used to refer to the position p, velocity v and attitude θ; is the observation matrix, used to map the state vector x k to the observation space k; v k is the observation noise, generally Gaussian white noise;

[0141] Therefore, in the fusion process, the IMU angular velocity and the fused velocity information update the state estimation through Kalman filtering, and combine with the Newton kinematic model to predict the next state, so as to output an accurate positioning result.

[0142] In summary, based on the above adaptive sliding window anomaly detection module and fuzzy logic weight adjustment module, the positioning system executes the fuzzy inference process in the positioning method:

[0143] The inference method adopts the Mandani fuzzy inference method, uses the min operator to process the AND relationship in the rules, and the max operator to process the OR relationship;

[0144] Defuzzification method: The centroid method is used for defuzzification to ensure the continuity and differentiability of the weight output.

[0145] In addition, the weight update and historical record maintenance are processed using the following operation process:

[0146] Update the IMU weight according to the result of defuzzification, and calculate the odometer weight at the same time (odometer weight = 1 - IMU weight). At this time, ensure that the IMU weight is within the range of [0.3, 0.9], and save the new IMU weight to the historical record for reference during the next weight adjustment.

[0147] In addition, in summary, this method adopts a lightweight algorithm, which is applicable to various types of mobile robots and autonomous navigation vehicles, is convenient to implement and run in an embedded system, and has high computing efficiency and strong versatility.

[0148] At the same time, in summary, the method of the present invention combines the use of a sliding window with multi-modal anomaly detection and fuzzy logic, and performs well in dynamic, complex and anomaly-prone environments. Compared with traditional fixed window or static weight methods, it has stronger environmental adaptability and anomaly handling capabilities.

[0149] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which when executed by a processor causes the processor to execute the steps of the above-mentioned method.

[0150] In yet another aspect, the present invention also discloses a computer device including a memory and a processor, where the memory stores a computer program, which when executed by the processor causes the processor to execute the steps of the above method.

[0151] In yet another embodiment provided by the present application, there is also provided a computer program product containing instructions, which when running on a computer causes the computer to execute any of the IMU-odometer fusion positioning methods in the above embodiments.

[0152] It can be understood that the system provided by the embodiments of the present invention corresponds to the method provided by the embodiments of the present invention. For the explanations, examples, and beneficial effects of relevant content, reference can be made to the corresponding parts in the above method.

[0153] The embodiments of the present application also provide an electronic device including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus.

[0154] The memory is used to store a computer program.

[0155] The processor is used to implement the above-mentioned IMU-odometer fusion positioning method when executing the program stored on the memory.

[0156] The communication bus mentioned in the above electronic device may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0157] The communication interface is used for communication between the above electronic device and other devices.

[0158] The memory may include a Random Access Memory (RAM), and may also include a Non-Volatile Memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located far from the aforementioned processor.

[0159] The above-mentioned processor may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0160] It should also be noted that the electronic device further includes a terminal device, which can also be referred to as a terminal, user equipment, mobile station, mobile terminal, etc. The terminal device can be a mobile phone, smart TV, wearable device, tablet computer, computer with wireless transceiver function, virtual reality terminal device, augmented reality terminal device, wireless terminal in industrial control, wireless terminal in unmanned driving, wireless terminal in remote surgery, wireless terminal in smart grid, wireless terminal in transportation safety, wireless terminal in smart city, wireless terminal in smart home, and so on. The embodiments of the present application do not limit the specific technologies and specific device forms adopted by the terminal device.

[0161] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general computer, a special computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, DSL) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state drive SSD), etc.

[0162] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

[0163] In addition, it should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of the present invention, the directional indications are only used to explain the relative position relationship and movement conditions between components in a specific posture. If the specific posture changes, the directional indications will also change accordingly.

[0164] In addition, if descriptions such as "first" and "second" are involved in the embodiments of the present invention, such descriptions of "first", "second", etc. are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the meaning of "and / or" that appears throughout the text includes three parallel scenarios. Taking "A and / or B" as an example, it includes Scenario A, or Scenario B, or the scenario where both A and B are satisfied simultaneously. In addition, in the embodiments of the present invention, "a plurality of" means two or more. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

Claims

1. An IMU-odometer fusion positioning method, characterized in that, It includes the following steps: S1. Collect the angular velocity data and acceleration data of the vehicle body movement through the IMU, and collect the rotational speed information of the wheels through the odometer; S2. Inertially solve the data collected by the IMU to obtain speed information, and combine the data collected by the odometer for multi-level anomaly detection to identify the abnormal result of the acquired data; S3. Input the abnormal result of the data for fuzzy inference to dynamically adjust the weights of the data collected by the IMU and the odometer during the fusion process, and obtain the speed information after fusion processing by weighted summation according to the weights; S4. Output the accurate positioning result after fusing the angular velocity data collected by the IMU and the speed information after fusion processing through Kalman filtering.

2. The IMU-odometer fusion positioning method according to claim 1, characterized in that, The specific operation process of the multi-level anomaly detection in step S2 includes: S21. Preset the threshold of the motion state and the sampling points of the sliding window, dynamically adjust the size of the sliding window according to the speed information, the data collected by the odometer and the preset motion state threshold, and update the data in the window; S22. Perform multi-modal anomaly detection based on the sliding window data; S23. According to the multi-modal anomaly detection results, assign preset weights to different result features according to the preset importance; S24. According to the assigned weights, perform weighted summation on the multi-modal anomaly detection result features to obtain the overall anomaly detection score.

3. The IMU-odometer fusion positioning method according to claim 2, characterized in that, The multi-modal anomaly detection in step S22 includes: (1) Compare the speed characteristics within the sliding window and identify abnormal fluctuations based on the time-domain change; (2) Perform fast Fourier transform on the data within the sliding window, extract the main frequency components and their amplitudes, and detect abnormal frequency patterns; (3) Perform matching based on the template of historical data, and use the support vector machine (SVM) model to output the abnormal probability.

4. The IMU-odometer fusion positioning method according to claim 1, wherein The specific operation process of the fuzzy inference in step S3 includes: S31. Input the abnormal result of the data, and set the membership function of the corresponding data according to the preset data interval for fuzzy processing; S32. According to the abnormal result of the data after fuzzy processing, establish a fuzzy rule base including a normal working condition rule group and an abnormal working condition rule group; S33. Use the Mandani fuzzy inference method for the abnormal result of the data, and then use the centroid method for defuzzification processing to ensure the continuity and differentiability of the weight output; S34. Update the IMU weight according to the data result after defuzzification processing, and calculate the odometer weight at the same time.

5. The IMU-odometer fusion positioning method according to claim 4, characterized in that In step S33, the Mandani fuzzy inference method processes the AND relationship in the fuzzy rule base through the min operator and the OR relationship in the fuzzy rule base through the max operator.

6. The IMU-odometer fusion positioning method according to claim 4, characterized in that The odometer weight in step S34 is obtained through the following calculation formula: odometer weight = 1 - IMU weight; And the odometer weight is within the specified range.

7. The IMU-odometer fusion positioning method according to claim 1, wherein The specific operation process of the Kalman filter fusion in step S4 includes: S41. Define the movement state vector including the pose, attitude, and speed of the vehicle body; S42. Based on the Newtonian kinematic model, use the current movement state vector of the vehicle body to predict the next state of the vehicle body; S43. Based on the angular velocity data collected by the IMU and the velocity information after fusion processing, substituting the predicted value of the next state of the vehicle body into the Kalman filter, an accurate positioning result can be obtained and output.

8. An IMU-odometer integrated positioning system, characterized in that, The IMU-odometer fusion positioning method for executing any one of the above claims 1-7 includes: A sensor module composed of an IMU and an odometer; An adaptive sliding window anomaly detection module for performing multi-level anomaly detection operations to identify and obtain data anomaly results; A fuzzy logic weight adjustment module for performing fuzzy inference operations on the data anomaly results; A Kalman filter module for outputting a positioning result after fusing multi-source data.

9. A computer-readable storage medium, characterized in that, Stored with a computer program, when the computer program is executed by a processor, the processor is caused to execute the steps of the method according to any one of claims 1 to 7.

10. A computer device, characterized in that, Including a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to execute the steps of the method according to any one of claims 1 to 7.

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