連続ドメイン適応と最適輸送を用いた深層慣性ナビゲーション
Deep Inertial Navigation using Continuous Domain Adaptation and Optimal Transport
車輪ロボットの慣性ナビゲーションにおいて、センサ位置の変化による信号分布のずれを連続ドメイン適応と最適輸送で補正し、汎化性能を高める手法を提案した論文。
著者: Mohammed Alloulah, Maximilian Arnold, Anton Isopoussu
分類: cs.LG, cs.RO
原文アブストラクト
In this paper, we propose a new strategy for learning inertial robotic navigation models. The proposed strategy enhances the generalisability of end-to-end inertial modelling, and is aimed at wheeled robotic deployments. Concretely, the paper describes the following. (1) Using precision robotics, we empirically characterise the effect of changing the sensor position during navigation on the distribution of raw inertial signals, as well as the corresponding impact on learnt latent spaces. (2) We propose neural architectures and algorithms to assimilate knowledge from an indexed set of sensor positions in order to enhance the robustness and generalisability of robotic inertial tracking in the field. Our scheme of choice uses continuous domain adaptation (DA) and optimal transport (OT). (3) In our evaluation, continuous OT DA outperforms a continuous adversarial DA baseline, while also showing quantifiable learning benefits over simple data augmentation. We will release our dataset to help foster future research.
