ロボットプラットフォームナビゲーションのための階層ベイズデータ融合
Hierarchical Bayesian Data Fusion for Robotic Platform Navigation
複数のセンサ計測を階層ベイズ的に統合し、局所統計と軟多数決投票で適応的に融合する手法を提案。シミュレーションと2つのロボットで有効性を検証した。
著者: Andres F. Echeverri, Henry Medeiros, Ryan Walsh, Yevgeniy Reznichenko, Richard Povinelli
分類: cs.RO
原文アブストラクト
Data fusion has become an active research topic in recent years. Growing computational performance has allowed the use of redundant sensors to measure a single phenomenon. While Bayesian fusion approaches are common in general applications, the computer vision field has largely relegated this approach. Most object following algorithms have gone towards pure machine learning fusion techniques that tend to lack flexibility. Consequently, a more general data fusion scheme is needed. Within this work, a hierarchical Bayesian fusion approach is proposed, which outperforms individual trackers by using redundant measurements. The adaptive framework is achieved by relying on each measurement's local statistics and a global softened majority voting. The proposed approach was validated in a simulated application and two robotic platforms.
