Adaptive Kalman Filter Localization Algorithm for Wheeled Robots for Underground Drainage Networks

To improve the positioning accuracy of pipeline robots in underground pipeline networks with diameters ranging from 400 to 600 mm, data from low-cost wheel encoders, optical flow sensors, and six-axis inertial measurement units are integrated to achieve robot localization in the absence of global information, thereby estimating a more precise trajectory of the robot movement. Firstly, an outlier detection mechanism is introduced to address the common issue of wheel slippage in pipeline environments, and the data from wheel encoders are corrected by setting a threshold for outlier judgment. Secondly,the Taguchi method is employed to assist in adjusting the parameters of the process noise covariance matrix in the Kalman filtering process, achieving an efficiency 5 times that of the trial-and-error method. Additionally, an adaptive parameter adjustment mechanism is implemented by incorporating residuals, a forgetting factor, and parameter thresholds, to tackle the adjustment of the measurement noise covariance matrix. Finally, experiments are conducted on a physical platform.The results demonstrate that the improved algorithm effectively suppresses errors caused by longitudinal slippage, with a positioning accuracy 3.26 times that of the extended Kalman filter-based positioning algorithm.