Encoder and IMU fusion indoor positioning method based on improved ESKF

Abstract

The invention provides an improved ESKF-based encoder and IMU fusion indoor positioning method, and aims to improve the positioning precision and control stability of an automatic guided vehicle (AGV) or other mobile robots in a dynamic environment. According to the method, the adaptive characteristic of fuzzy logic and the nonlinear state estimation capability of the ESKF are combined, and uncertainty compensation and dynamic adjustment of multi-sensor data are realized. In the implementation process, firstly, a motion state of a system is modeled by using an encoder and IMU data, and data alignment is realized through a time synchronization mechanism; and then, a fuzzy logic control system is introduced, and a process noise covariance matrix and an observation noise covariance matrix of the ESKF are dynamically adjusted according to the change of the sensor noise, so that the filtering performance is optimized. In the ESKF prediction step, encoder data is used as main input, and the vehicle state is estimated through a kinematic model; in the updating step, state estimation is further corrected in combination with angular velocity and acceleration information provided by the IMU so as to enhance the adaptivity and robustness of the algorithm. Through experimental verification, the method shows the excellent performance of accurate fusion of the AGV motion state in a complex dynamic environment, and effectively improves the positioning precision and the real-time response capability of the system.

Description

Technical Field

[0001] The present invention belongs to the field of automatic control and navigation positioning technology, and specifically relates to an indoor positioning method based on an improved ESKF encoder and IMU fusion, which is used to improve the positioning accuracy and stability of mobile robots (such as AGVs) in complex environments. By combining fuzzy control with adaptive algorithms, effective compensation and optimization of positioning errors in dynamic environments are achieved, which is suitable for scenarios with high requirements for high accuracy and robustness. Background Art

[0002] In the field of automatic navigation and positioning, with the continuous improvement of robot technology and automation level, mobile robots (such as automatic guided vehicles AGV) are increasingly widely used in industrial production, logistics and transportation. Accurate positioning is one of the key technologies for mobile robots to achieve efficient and stable navigation and operation. However, due to various uncertain factors in complex environments (such as uneven ground, sensor errors and external interference), the accuracy and robustness of traditional positioning methods are often limited.

[0003] The existing positioning methods mainly include the odometer method based on wheel encoders, the inertial navigation method based on inertial measurement units (IMUs), and the combined navigation method that integrates multiple sensors.

[0004] Encoder method: Relies on wheel speed information to calculate displacement, but is easily affected by factors such as tire slippage and cumulative errors, resulting in a significant increase in positioning errors after long-term operation.

[0005] IMU method: It achieves short-term high-precision positioning by measuring acceleration and angular velocity, but the result will be affected by gyroscope drift and accelerometer noise, resulting in a gradual increase in cumulative errors.

[0006] Multi-sensor fusion methods: such as extended Kalman filter (EKF) and particle filter (PF), can combine multiple sensor data to make up for the shortcomings of a single sensor, and are widely used to improve positioning accuracy and stability. However, in dynamic environments and complex working conditions, traditional filter models are difficult to adapt to rapidly changing error characteristics, and positioning accuracy may still be limited.

[0007] In addition, with the dynamic changes of the environment, the source of positioning error has time-varying and nonlinear characteristics. Traditional parameter fixed filtering algorithms are difficult to cope with this complexity. Therefore, fuzzy control and adaptive adjustment methods have gradually become important technical means to solve such problems. By introducing fuzzy rules and dynamic parameter adjustment mechanisms in the filtering process, the system's ability to suppress errors and adaptability can be significantly improved.

[0008] Based on this, the present invention proposes a positioning method based on error state extended Kalman filter (ESKF) and fuzzy adaptive control based on IMU and encoder fusion to improve the positioning accuracy and robustness in complex dynamic environments. Summary of the invention

[0009] The present invention relates to the field of automatic control and navigation technology, in particular to a positioning method based on an error state extended Kalman filter (ESKF) and fuzzy adaptive control fused with an IMU and an encoder, which is used to improve the positioning accuracy and stability of a mobile robot in a complex environment. In order to achieve the above object, the present invention provides the following technical solutions:

[0010] IMU and encoder data fusion: Through the error state extended Kalman filter (ESKF), the acceleration and angular velocity data provided by the IMU are fused with the wheel speed and position information provided by the encoder. This method is based on the kinematic model and observation model, preliminarily eliminates the influence of sensor noise, and effectively reduces the cumulative error caused by a single sensor.

[0011] Fuzzy adaptive filtering mechanism: Introduce fuzzy control rules to dynamically adjust the process noise and observation noise covariance matrix of the Kalman filter according to the system positioning error and its rate of change. Through fuzzy reasoning and parameter adaptive adjustment, the filter can adapt to the noise changes in complex environments in real time, thereby improving the filtering accuracy and system robustness.

[0012] Nonlinear error compensation: Aiming at nonlinear error sources such as tire slippage and uneven ground, an error compensation module is designed by combining fuzzy reasoning and extended state estimation to dynamically correct positioning deviations, further optimize the pose estimation results, and improve the reliability of the positioning system.

[0013] Dynamic environment adaptability: Combined with fuzzy adaptive control methods, the positioning system can quickly respond to changes in the external environment, such as dynamic obstacles, vibrations and other interference. By adjusting the filter parameters and model weights in real time, the system can maintain high-precision performance under different working conditions.

[0014] Experimental verification: The effectiveness of the proposed method is verified through multi-scenario experiments, including simulation environments and actual industrial application scenarios, and the positioning accuracy, robustness and real-time performance are tested respectively. The experimental results show that the proposed method significantly improves the positioning performance of mobile robots in complex environments.

[0015] The present invention proposes an error state extended Kalman filter based on IMU and encoder fusion

[0016] The positioning method of (ESKF) and fuzzy adaptive control has the following advantages:

[0017] High precision: Through multi-source sensor data fusion, sensor noise and cumulative errors are significantly reduced, providing high-precision pose estimation.

[0018] Strong robustness: Fuzzy adaptive control dynamically adjusts filter parameters to effectively deal with nonlinear interference and noise changes in complex environments.

[0019] Environmental adaptability: Able to quickly respond to dynamic environmental changes and suitable for a variety of complex working conditions in industrial scenarios.

[0020] Easy scalability: The method is applicable to different types of mobile robot positioning systems and has wide application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Attached Figure 1 : System architecture diagram of indoor positioning method based on encoder and IMU fusion of improved ESKF.

[0022] Specific implementation methods The specific implementation methods of the present invention are described below in conjunction with the accompanying drawings:

[0023] In the implementation of positioning and attitude estimation based on ESKF (Error State Extended Kalman Filter) combined with encoder and IMU data, the sensor data needs to be thoroughly preprocessed first, including noise filtering, zero bias correction and time synchronization to ensure data quality and consistency. Next, the state and covariance matrix of ESKF are initialized to prepare for the operation of the filter. Then, the IMU data, including acceleration and angular velocity, are read in real time. The displacement is obtained by integrating the acceleration data, and the rotation angle is obtained by integrating the angular velocity data. These steps constitute the prediction phase of ESKF. In the prediction phase, the system state is updated according to the IMU data to predict the state at the next moment. At the same time, the encoder data is read in real time to obtain the linear velocity and angular velocity of the two wheels. This data is used to calculate the movement distance and yaw angle change in the world coordinate system, which will be used in the measurement update phase of ESKF. In the measurement update phase, the encoder data is fused with the predicted state to correct the error generated in the prediction. The Kalman gain is adaptively adjusted through fuzzy logic, and the gain is dynamically adjusted according to the error and uncertainty to improve the accuracy of the estimation. In addition, the state and covariance matrix are updated to prepare for the next iteration. This step ensures that the filter can continuously track changes in the system state and adapt to possible dynamic changes. Considering the dynamic and real-time requirements of the system, an efficient data processing and state update mechanism is introduced to enable the model to respond quickly to changes in sensor data. Finally, the position and attitude estimates of the AG (which may refer to an autonomous robot or similar device) are output, and these estimation results are optimized to ensure efficient deployment and performance feedback in practical applications. The model is evaluated through a series of performance indicators and iteratively optimized based on the evaluation results to ensure the accuracy and reliability of the model in the actual system. In this way, the ESKF-based positioning and attitude estimation system can provide accurate state information to support autonomous navigation and control.

Claims

1. An indoor positioning method based on improved ESKF encoder and IMU fusion, characterized in that include: By introducing a fuzzy logic control system, the process noise covariance matrix Q and the observation noise covariance matrix R of the error state extended Kalman filter (ESKF) are adaptively adjusted to correct the filter parameters in real time, thereby improving the robustness and accuracy of the positioning system according to the uncertainty changes of the encoder and IMU data. The ESKF state equation calculates the vehicle's motion state, including position, velocity, and attitude angle, using encoder data; the observation equation updates the state estimate using IMU data (accelerometer and gyroscope); Finally, the time synchronization mechanism is used to align the timestamps of the encoder and IMU data to ensure the consistency and real-time performance of data fusion.

2. The method according to claim 1, characterized in that ESKF's state equation calculates the vehicle's motion state based on encoder data, including position, speed and attitude angle; The observation equation updates the vehicle's state estimate based on the IMU data (accelerometer and gyroscope).

3. The method according to claim 1 or 2, characterized in that: The fuzzy logic control system analyzes the real-time changes of encoder noise and IMU noise and adaptively adjusts the values ​​of Q and R to ensure the filter's responsiveness to dynamic environmental changes.

4. The method according to claim 1 or 3, characterized in that: The sliding window method is used to perform statistical analysis on the IMU and encoder data over the past period of time, dynamically adjust the fuzzy logic input parameters, and improve the stability of the filter for long-term motion states. When the encoder or IMU signal is lost or the delay exceeds the set threshold, the fuzzy logic control system prioritizes adjusting the filter's weight to the trusted data source to maintain positioning accuracy.

Images