ドメイン間の物理運動転送によるニューラル慣性トラッキング
Transferring Physical Motion Between Domains for Neural Inertial Tracking
ラベル付きソースドメインから物理運動知識を転送し、ペアデータなしで新たな未ラベルドメインの生IMU系列を正確な慣性軌道に変換するフレームワークを提案した。
著者: Changhao Chen, Yishu Miao, Chris Xiaoxuan Lu, Phil Blunsom, Andrew Markham, Niki Trigoni
分類: cs.LG, cs.CV, cs.RO, stat.ML
原文アブストラクト
Inertial information processing plays a pivotal role in ego-motion 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 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 physical 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.
