MotionTransformer: Transferring Neural Inertial Tracking between Domains


  • Changhao Chen University of Oxford
  • Yishu Miao University of Oxford
  • Chris Xiaoxuan Lu University of Oxford
  • Linhai Xie University of Oxford
  • Phil Blunsom University of Oxford
  • Andrew Markham University of Oxford
  • Niki Trigoni University of Oxford



Inertial information processing plays a pivotal role in egomotion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent. However, they are affected greatly by changes in sensor placement/orientation or motion dynamics, and it is infeasible to collect labelled data from every domain. To overcome the challenges of domain adaptation on long sensory sequences, we propose MotionTransformer - a novel framework that extracts domain-invariant features of raw sequences from arbitrary domains, and transforms to new domains without any paired data. Through the experiments, we demonstrate that it is able to efficiently and effectively convert the raw sequence from a new unlabelled target domain into an accurate inertial trajectory, benefiting from the motion knowledge transferred from the labelled source domain. We also conduct real-world experiments to show our framework can reconstruct physically meaningful trajectories from raw IMU measurements obtained with a standard mobile phone in various attachments.




How to Cite

Chen, C., Miao, Y., Lu, C. X., Xie, L., Blunsom, P., Markham, A., & Trigoni, N. (2019). MotionTransformer: Transferring Neural Inertial Tracking between Domains. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 8009-8016.



AAAI Technical Track: Robotics