This research explores the impact of encoder resolution on the odometry accuracy and navigational performance of a differential-drive Autonomous Mobile Robot (AMR), using the Automated Trash Mobile Robot (ALTO) as a test platform. Encoder pulse-per-revolution (PPR) values ranging from 40 to 4096 were simulated in Gazebo. A custom encoder and odometry simulation algorithm were developed and integrated into the ROS1-based navigation stack. Controlled experiments—including straight-line, rotational, and dynamic path tests—were conducted in virtual environments to compare positional accuracy using /odom, /amcl_pose, /global_pose, and /world_pose. Results showed that higher PPR values improved odometry precision, particularly in orientation estimation, but had limited influence on global pose accuracy under AMCL-based sensor fusion. While lower resolutions caused noticeable drift, AMCL maintained robust localization. The findings offer practical guidance for optimizing encoder selection, balancing cost and performance in industrial AMR deployments.

Encoder resolution and sensor fusion architecture directly influence odometric drift in autonomous mobile robots (AMRs), yet their interaction with particle filter localization remains insufficiently characterized. This study evaluates four encoder resolutions (90, 512, 1024, and 4096 PPR) in wheel-odometry-only (System A) and EKF-based IMU-fused (System B) configurations across multiple navigation scenarios using ROS AMCL localization. Increasing resolution significantly reduced drift, with 512 PPR achieving most of the improvement observed at 4096 PPR and diminishing returns beyond 1024 PPR. IMU fusion consistently improved odometry accuracy and reduced variance, although its benefit depended on trajectory complexity rather than encoder resolution alone. However, improved odometry did not uniformly translate into improved localization. In several cases, the IMU-fused system exhibited reduced AMCL stability despite lower odometric drift. These results demonstrate that encoder selection and sensor fusion must be considered alongside particle filter parameterization, highlighting the importance of estimator compatibility in ROS-based AMR localization architectures.