Simulation-Based Evaluation of Robust Multi-Sensor Localization for Tracked Agricultural Robots Under Asymmetric Track Slip and Single-Coordinate GNSS Anomalies

Abstract

Tracked agricultural robots operating on soft ground may experience asymmetric left–right track slip, while two-dimensional global navigation satellite system (GNSS) observations may contain a dominant anomaly in one coordinate. This simulation study evaluates a robust multi-sensor localization method for this coupled degradation. Independent left–right slip ratios are introduced into a six-state extended Kalman filter (EKF) to improve motion prediction. A coordinate-adaptive Huber extended Kalman filter (CAH-EKF) is then developed for GNSS updating. It combines component-wise normalized innovations and innovation jumps for coordinate-level anomaly detection, maps Huber weights to effective measurement covariance, and uses finite-state switching to confirm, maintain, and recover robust processing while retaining information from the relatively reliable coordinate. Simulation experiments on S-shaped and multi-U paths separate prediction-stage and update-stage effects. The independent-slip model improves over the common-slip model by 16.99–34.34% in position root mean square error (RMSE) and 29.11–36.27% in heading RMSE. Under sustained single-coordinate GNSS anomalies, CAH-EKF outperforms IS-EKF and remains competitive with Huber-IEKF. Under the main joint degradation experiments, the combined method yields 42.78–77.43% position and 27.05–76.33% heading improvements. Additional simulation checks covering slip excitation, path and motion changes, GNSS-degradation variants, and runtime further characterize the operating range of the method. These results support robust low-cost localization using encoders, a yaw-rate gyroscope, and a two-dimensional GNSS within the tested simulation conditions.