Kalman Filter–Based Sensor Fusion for Navigation of Holonomic Unmanned Ground Vehicles

ABSTRACT Autonomous unmanned ground vehicles (UGVs) require accurate and reliable navigation, capable of working in adverse conditions for secure mission completion. Navigation modules such as the Global Positioning System (GPS) sometimes deviate in outdoor environments or fail in indoor environments. The degradation in navigation leads the UGV to deviate from its desired position. This paper addresses the challenge of interruption in navigation during a mission. The sensor fusion scheme using the estimation architecture of Kalman Filter (KF) enables the robot to navigate itself by fusing the data from GPS, encoders and Inertial Measurement Unit (IMU). The scheme navigates the robot within practical limits during the GPS denial. Following a review of the Meccanum Wheel Robot's model dynamics and kinematics, we synthesized the KF‐based sensor fusion scheme. Simulations and experiments are performed on different trajectories to evaluate the proposed scheme, with Root Mean Square Error as a quantitative measure. Results demonstrate the efficacy of KF‐based sensor fusion for navigation in the presence of GPS denial, thereby ensuring the successful completion of the mission.