Navigation, which is defined as determining the most convenient way to go from one point to another and make the journey on the planned path, is indispensable for developing a fully autonomous system. The navigation stack and its components are a widely studied area in the literature. However, in most of the studies, the developed approaches are tested in laboratory or office environments. Suppose such an approach is intended to be used in a real-world large-scale industrial application. In that case, it must operate to industry standards in a highly dynamic and ever-changing environment and show similar performance on every run or repetitive duty. In this study, for prototypes of industrial autonomous mobile robots (AMRs) equipped with encoders, inertial measurement unit (IMU) and laser rangefinders in order to navigate, an Extended Kalman Filter-based localization approach is implemented using the well-known packages available in the robot operating system (ROS) environment as a part of the navigation stack. Some enhancements are made by showing what kind of challenges may occur in a real factory environment. All developed and/or improved approaches have been tested and validated on the real automotive production lines of the Ford Otosan Kocaeli Plant, considering real working conditions such as real noises, disturbances and obstacles. This is the most important difference that distinguishes this study from similar publications in the literature.