リアルタイム動作分類による足装着型慣性ナビゲーションの精度向上
Improving Foot-Mounted Inertial Navigation Through Real-Time Motion Classification
足装着センサの慣性データからSVMで6種類の動作を90%以上の精度で分類し、動作ごとに最適化したゼロ速度検出パラメータを適用することで、歩行と走行が混在する屋内歩行者ナビゲーションの位置誤差を低減した。
著者: Brandon Wagstaff, Valentin Peretroukhin, Jonathan Kelly
分類: cs.RO, cs.HC
原文アブストラクト
We present a method to improve the accuracy of a foot-mounted, zero-velocity-aided inertial navigation system (INS) by varying estimator parameters based on a real-time classification of motion type. We train a support vector machine (SVM) classifier using inertial data recorded by a single foot-mounted sensor to differentiate between six motion types (walking, jogging, running, sprinting, crouch-walking, and ladder-climbing) and report mean test classification accuracy of over 90% on a dataset with five different subjects. From these motion types, we select two of the most common (walking and running), and describe a method to compute optimal zero-velocity detection parameters tailored to both a specific user and motion type by maximizing the detector F-score. By combining the motion classifier with a set of optimal detection parameters, we show how we can reduce INS position error during mixed walking and running motion. We evaluate our adaptive system on a total of 5.9 km of indoor pedestrian navigation performed by five different subjects moving along a 130 m path with surveyed ground truth markers.
