<p>This paper presents a fault detection and toleration scheme for Unmanned Ground Vehicles (UGVs) with two position sensors and orientation sensors. Four representative types of sensor faults are considered: complete fault, bias fault, drift fault, and precision degradation. The proposed detection method consists of a Long Short-Term Memory (LSTM) Network Module, an Amplitude Difference Thresholding Module, and an Actuation Motion Coherence Module. A Husarion Rosbot 2.0 and VICON motion capture system compose a platform that is used to collect motion data for network training and experimental validation of the proposed scheme. Sensor fault detection performance is experimentally validated using a trajectory that was not included in the training data set. The fault detection accuracy is compared to other learning-based fault detection methods. Based on the fault detection result, we propose the fault toleration method.</p>