Robots deployed in settings such as warehouses and parking lots must cope with frequent and substantial changes when localizing in their environments. While many previous localization and mapping algorithms have explored methods of identifying and focusing on long-term features to handle change in such environments, we propose a different approach -- can a robot understand the distribution of movable objects and relate it to observations of such objects to reason about global localization? In this paper, we present probabilistic object maps (POMs), which represent the distributions of movable objects using pose-likelihood sample pairs derived from prior trajectories through the environment and use a Gaussian process classifier to generate the likelihood of an object at a query pose. We also introduce POM-Localization, which uses an observation model based on POMs to perform inference on a factor graph for globally consistent long-term localization. We present empirical results showing that POM-Localization is indeed effective at producing globally consistent localization estimates in challenging real-world environments and that POM-Localization improves trajectory estimates even when the POM is formed from partially incorrect data.

翻译：在仓库和停车场等环境中部署的机器人在环境定位时必须应对频繁和实质性变化。虽然许多以前的本地化和绘图算法已经探索了识别和关注长期特征的方法,以应对这些环境中的变化,但我们提议了一种不同的方法 -- -- 机器人能够理解移动物体的分布,并将它与此类物体的观测与全球定位的合理性联系起来吗?在本文件中,我们提供了概率物体图,这些图代表了移动物体的分布,使用了通过环境通过先前的轨迹生成的相貌相似的样本配对,并使用了高山进程分类法来生成一个被查询对象的可能性。我们还采用了POM-本地化方法,该方法使用基于POMs的观测模型来推导出一个全球一致的长期本地化要素图。我们介绍了实证结果,显示POM-本地化在挑战真实世界环境中确实能够产生全球一致的本地化估计值,而POM-本地化即使从部分不正确的数据中形成POM,也能改进轨迹估计。