連続時間連続体ロボットのオンライン状態推定のためのスライディングウィンドウフィルタ
A Sliding-Window Filter for Online Continuous-Time Continuum Robot State Estimation
連続体ロボットの連続時間状態推定をオンラインで実行するスライディングウィンドウフィルタを提案し、リアルタイムより高速に動作することを示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Spencer Teetaert, Sven Lilge, Jessica Burgner-Kahrs, Timothy D. Barfoot
分類: cs.RO
原文アブストラクト
Stochastic state estimation methods for continuum robots (CRs) often struggle to balance accuracy and computational efficiency. While several recent works have explored sliding-window formulations for CRs, these methods are limited to simplified, discrete-time approximations and do not provide stochastic representations. In contrast, current stochastic filter methods must run at the speed of measurements, limiting their full potential. Recent works in continuous-time estimation techniques for CRs show a principled approach to addressing this runtime constraint, but are currently restricted to offline operation. In this work, we present a sliding-window filter (SWF) for continuous-time state estimation of CRs that improves upon the accuracy of a filter approach while enabling continuous-time methods to operate online, all while running at faster-than-real-time speeds. This represents the first stochastic SWF specifically designed for CRs, providing a promising direction for future research in this area.
