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Recently, data-driven inertial navigation approaches have demonstrated theircapability of using well-trained neural networks to obtain accurate positionestimates from inertial measurement units (IMU) measurements. In this paper, wepropose a novel robust Contextual Transformer-based network for InertialNavigation~(CTIN) to accurately predict velocity and trajectory. To this end,we first design a ResNet-based encoder enhanced by local and global multi-headself-attention to capture spatial contextual information from IMU measurements.Then we fuse these spatial representations with temporal knowledge byleveraging multi-head attention in the Transformer decoder. Finally, multi-tasklearning with uncertainty reduction is leveraged to improve learning efficiencyand prediction accuracy of velocity and trajectory. Through extensiveexperiments over a wide range of inertial datasets~(e.g. RIDI, OxIOD, RoNIN,IDOL, and our own), CTIN is very robust and outperforms state-of-the-artmodels.

CTIN: Robust Contextual Transformer Network for Inertial Navigation