Real-to-Sim Calibration and Cross-Domain Trajectory Validation of a Low-Cost Multi-Sensor UGV Digital Twin

Abstract

Bridging the real-to-sim gap in low-cost autonomous mobile robotics requires careful cross-domain alignment of kinematic geometry, actuator behavior, and sensor characteristics. This paper presents a systematic Real-to-Sim parameter calibration and multi-stage experimental validation framework for a low-cost differential-drive unmanned ground vehicle (Jackson UGV) operating within NVIDIA Isaac Sim. The calibration process distinguishes initial product/design references, directly measured physical geometry, empirically adjusted ROS 2 runtime parameters, and simulation-specific PhysX parameters. By tuning virtual wheel geometry, inertial sensor profiles, and PhysX joint-drive damping, the proposed framework enables controlled comparison between physical execution and digital-twin behavior. Benchmark evaluations across three experimental stages—square waypoint-tracking trajectories, continuous figure-eight maneuvers, and dynamic obstacle avoidance in a mapped maze course—quantify rotational repeatability, temporal alignment, estimator consistency, and cross-domain trajectory deviation. The square and figure-eight trials reveal a proprioceptive “estimator optimism gap” in which onboard EKF estimates remain internally repeatable while underestimating terminal displacement relative to external floor measurements or simulator-provided reference poses. In Stage 3, 2D LiDAR-based localization and Nav2/DWB local planning reduce dependence on purely proprioceptive dead reckoning, achieving 100% goal completion without observed collision events across both physical and virtual deployments. The results support the calibrated digital twin as a controlled simulation baseline for studying cross-domain navigation behavior and for future sim-to-real evaluation of autonomous mobile robot navigation algorithms.