フットマウント慣性航法のためのロバストなデータ駆動型零速度検出
Robust Data-Driven Zero-Velocity Detection for Foot-Mounted Inertial Navigation
足装着慣性センサの航法精度向上のため、運動分類器で閾値を適応更新する零速度検出法と、LSTMで生データから零速度を分類する手法を提案し、歩行・走行・階段昇降で既存法より高精度を示した。
著者: Brandon Wagstaff, Valentin Peretroukhin, Jonathan Kelly
分類: cs.RO, eess.SP
原文アブストラクト
We present two novel techniques for detecting zero-velocity events to improve foot-mounted inertial navigation. Our first technique augments a classical zero-velocity detector by incorporating a motion classifier that adaptively updates the detector's threshold parameter. Our second technique uses a long short-term memory (LSTM) recurrent neural network to classify zero-velocity events from raw inertial data, in contrast to the majority of zero-velocity detection methods that rely on basic statistical hypothesis testing. We demonstrate that both of our proposed detectors achieve higher accuracies than existing detectors for trajectories including walking, running, and stair-climbing motions. Additionally, we present a straightforward data augmentation method that is able to extend the LSTM-based model to different inertial sensors without the need to collect new training data.
