iMHS：一种增量式多假设平滑器
iMHS: An Incremental Multi-Hypothesis Smoother
摘要：
多模态混杂系统的状态估计是野外机器人领域的一个重要问题。然而，将离散模式的估计过程中受到阻碍的一个潜在的组合增长的计算。在本文中，我们提出了一种新的增量多假设平滑的基础上消除混合因子图到多假设贝叶斯树，它表示可能的离散状态序列假设。在iSAM之后，我们通过调节过去对未来的增量推理，但我们增加了维护多个离散模式历史的能力，利用问题的时间结构来获得将多假设树与贝叶斯树统一起来的简化表示。在结果部分，我们证明了算法的通用性与三个问题域的例子：车道变换检测（1D），飞机机动检测（2D），接触检测足式机器人（3D）。
英文摘要：
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).
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