让机器人预测环境变化，应对半静态物体的消失与重现。
Perpetua: Multi-Hypothesis Persistence Modeling for Semi-Static Environments
- 用贝叶斯框架融合多种假设，追踪物体消失与重现的可能性。
- 在模拟和真实数据上表现更准，且能实时适应缺失观测。
- 适合长期运行的机器人系统，尤其在动态变化环境中。
许多机器人系统需在复杂动态环境中长时间运行，其间环境部分会发生变化。现有建图或环境建模算法难以有效表示动态特征，通常通过滤除或加权平均处理异常状态观测。本文提出Perpetua方法，用于建模半静态特征的动态行为：可融入已有动态先验知识，跟踪多个假设，并随时间自适应以预测未来状态。具体地，通过串联混合的“持续性”与“新生”滤波器，在形式化贝叶斯框架中建模特征消失或重现的概率。该方法高效、可扩展、通用且鲁棒，能同时估计当前及任意未来时刻的特征状态。在模拟与真实数据上的实验表明，Perpetua相比同类方法精度更高，具备在线适应能力，对缺失观测也具有强鲁棒性。
原文摘要 · Abstract (English)
Many robotic systems require extended deployments in complex, dynamic environments. In such deployments, parts of the environment may change between subsequent robot observations. Most robotic mapping or environment modeling algorithms are incapable of representing dynamic features in a way that enables predicting their future state. Instead, they opt to filter certain state observations, either by removing them or some form of weighted averaging. This paper introduces Perpetua, a method for modeling the dynamics of semi-static features. Perpetua is able to: incorporate prior knowledge about the dynamics of the feature if it exists, track multiple hypotheses, and adapt over time to enable predicting of future feature states. Specifically, we chain together mixtures of "persistence" and "emergence" filters to model the probability that features will disappear or reappear in a formal Bayesian framework. The approach is an efficient, scalable, general, and robust method for estimating the states of features in an environment, both in the present as well as at arbitrary future times. Through experiments on simulated and real-world data, we find that Perpetua yields better accuracy than similar approaches while also being online adaptable and robust to missing observations.
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