Autonomous Robot Localization Using Advanced Sensor Fusion Techniques

Abstract

Autonomous robot localization is a critical function that enables intelligent navigation, motion planning, and interaction within structured and unstructured environments. Before 2019, significant advancements were made by integrating multiple sensors such as wheel encoders, IMUs, LiDAR, cameras, ultrasonic sensors, and GNSS. Since each sensor has limitations like drift, uncertainty, and environmental sensitivity, advanced sensor fusion techniques were developed to improve localization accuracy and reliability. This study examines localization methods based on probabilistic filtering approaches including Kalman Filter, Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF), and graph-based optimization. A multi-layer sensor fusion architecture combining odometry, inertial sensing, LiDAR, and vision-based observations is proposed for accurate robot pose estimation in dynamic environments. Experimental results demonstrate that multi-sensor fusion significantly improves localization accuracy, reduces drift, enhances robustness against sensor failures, and increases adaptability in indoor environments. Metrics such as RMSE, trajectory consistency, heading accuracy, covariance stability, and computational efficiency were used for evaluation. The integration of LiDAR, IMU, and wheel odometry reduced localization error by over 90% compared to wheel odometry alone, while vision-based loop closure further improved map consistency. Overall, the study highlights that advanced sensor fusion techniques provide an effective and reliable solution for autonomous robot localization and continue to influence modern robotic navigation systems.