Optimized Kalman Gain for Enhanced Localization in Differential Drive Robots - Amorim, Josef G. J. C.; Pinto, Milena F.; Castro, Gabriel G.R.; Santos, Tatiana M.B.; Canela, Andre L.C.; Amorim, Johann S. J. C. C. (Anais do Simpósio Brasileiro de Robótica e Simpósio Latino Americano de Robótica (SBR/LARS); 2025: Anais do XVII Simpósio Brasileiro de Robótica e XVI Workshop de Robótica na Educação, 2025)
Autonomous mobile robots require accurate localization and efficient obstacle-avoidance strategies to navigate complex environments. This work presents a navigation system that integrates data from an Inertial Measurement Unit (IMU) and wheel encoders using sensor fusion techniques to enhance localization accuracy. The proposed approach is implemented on a Nexus Mecanum 4WD robot and tested within the Robot Operating System (ROS) framework. Its effectiveness is evaluated through a series of experiments comparing raw sensor readings, unit gain filtering, and optimized Kalman gain filtering. Results show that the Extended Kalman Filter (EKF) reduces position estimation errors by up to 47% compared to unfiltered sensor data, achieving an average localization error of 0.08 meters over a to-meter trajectory. These results confirm the robustness and effectiveness of the proposed approach for enhancing localization accuracy in autonomous ground robots.