A Dataset for Robot Navigation in GNSS-Disrupted Environments Using Low-Cost Sensors

Global Navigation Satellite Systems (GNSS) have significantly improved the accuracy and availability of Positioning, Navigation, and Timing (PNT) systems; however, this has also exposed vulnerabilities in critical infrastructures due to their dependence on these systems. The emergence of autonomous vehicles further intensifies the criticality of this dependence, particularly in military scenarios where robust and resilient navigation and positioning data are essential. This paper addresses the resilience of PNT systems in unknown and dynamic environments with compromised GNSS signals. We propose a novel dataset to test navigation algorithms in scenarios with intermittent GNSS availability, where the remaining available signals consist of IMU data and wheel odometry. This dataset comprises different data acquired in trials with increasing degrees of difficulty with respect to the localization procedure, and we employ an Extended Kalman Filter to provide some localization results using these data.