Extended Kalman Filter-Based 2D Pose Estimation for Omnidirectional Mecanum Robots via Sensor Fusion: A SO(2) Lie Group Formulation

This article presents a 2D pose estimation method for an omnidirectional mobile robot with Mecanum wheels, using an extended Kalman filter (EKF) formulated on the Lie group SO(2). The purpose is estimate the robot’s position and orientation by fusing angular velocity measurements from the wheel encoders with data from an IMU. Employing Lie algebra, the EKF provides a consistent and compact representation of rotational motion, improving prediction and update steps. The filter was implemented in ROS 1 and validated in simulation using Gazebo, with a reference trajectory and real measurements used for evaluation. The system delivers higher pose estimation precision, validating the effectiveness in rotational maneuvers.