Probabilistic Object Maps for Long-Term Robot Localization
Probabilistic Object Maps for Long-Term Robot Localization
复制标题
DOI：
10.1109/iros47612.2022.9981316
复制
发表时间：
2021-09
期刊：
影响因子：
--
通讯作者：
Amanda Adkins;Joydeep Biswas
中科院分区：
文献类型：
--
作者：
Amanda Adkins;Joydeep Biswas
部署在仓库和停车场等场所的机器人，在环境本地化时必须应对频繁而重大的变化。虽然许多以前的定位和映射算法已经探索了识别和关注长期特征的方法来处理这种环境中的变化，但我们提出了一种不同的方法——机器人能否理解可移动物体的分布，并将其与对这些物体的观察联系起来，从而推断出全局定位？在本文中，我们提出了概率对象映射（POMs），它使用来自环境中先前轨迹的姿态-似然样本对来表示可移动对象的分布，并使用高斯过程分类器来生成对象在查询姿态处的似然。我们还介绍了POM-Localization，它使用基于pom的观测模型对因子图进行推理，以实现全局一致的长期定位。我们提出的实证结果表明，在具有挑战性的现实环境中，POM- localization确实可以有效地产生全局一致的定位估计，并且即使POM是由部分不正确的数据形成的，POM- localization也可以改善轨迹估计。
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.