iMHS：増分型マルチ仮説スムーザ
iMHS: An Incremental Multi-Hypothesis Smoother
離散モードを持つハイブリッド系の状態推定を、ハイブリッド因子グラフをマルチ仮説ベイズ木に変換し、複数の離散モード履歴を保持しながら増分推論する手法を提案した論文。
著者: Fan Jiang, Varun Agrawal, Russell Buchanan, Maurice Fallon, Frank Dellaert
分類: cs.RO
原文アブストラクト
State estimation of multi-modal hybrid systems is an important problem with many applications in the field robotics. However, incorporating discrete modes in the estimation process is hampered by a potentially combinatorial growth in computation. In this paper we present a novel incremental multi-hypothesis smoother based on eliminating a hybrid factor graph into a multi-hypothesis Bayes tree, which represents possible discrete state sequence hypotheses. Following iSAM, we enable incremental inference by conditioning the past on the future but we add to that the capability of maintaining multiple discrete mode histories, exploiting the temporal structure of the problem to obtain a simplified representation that unifies the multiple hypothesis tree with the Bayes tree. In the results section we demonstrate the generality of the algorithm with examples in three problem domains: lane change detection (1D), aircraft maneuver detection (2D), and contact detection in legged robots (3D).
