Pose Estimation of a Differential-Drive Mobile Robot Using EKF-Based Sensor Fusion

Localization is an important process in navigation systems. In this stage, the robot gathers the necessary data to navigate safely and reach its destination. This paper presents a robust pose estimation method for a differential-drive robot using sensor fusion techniques, along with the design, instrumentation, and implementation in a warehouse robot used for transporting loads, focusing on real-world applications. The objective is to accurately determine the robot’s position and orientation by integrating data from wheel encoders and an Inertial Measurement Unit (IMU) mounted on the robot. The wheel encoders allow the estimation of the linear and angular velocities of the robot, while the IMU offers complementary data on the robot’s acceleration and angular rates. The data is fused through the Extended Kalman Filter (EKF), considering the kinematics of the differential-drive robot and the uncertainties of the sensor measurements. The results from simulations and real-world experiments demonstrate an improvement in the pose estimation accuracy.