策划长期矢量地图
Curating Long-term Vector Maps
摘要：
自主服务移动机器人需要在人类环境中一致，准确和鲁棒地定位，尽管随着时间的推移，这种环境会发生变化。情景非马尔可夫定位通过将观测分类为长期、短期或动态特征来解决在这种不断变化的环境中定位的挑战。然而，为了做到这一点，EnML依赖于不随时间变化的长期矢量图（LTVM）的估计。在本文中，我们引入了一个递归算法来建立和更新LTVM随着时间的推移，通过推理多个机器人部署观察到的对象的可见性约束。我们使用一个有符号的距离函数（SDF），从机器人的多次部署中过滤出短期和动态特征的观察。剩余的长期观测值用于通过鲁棒局部线性回归构建矢量图。在由此产生的LTVM的不确定性计算通过Monte Carlo响应所产生的长期功能的观测。通过将基于占用网格的SDF观测过滤与过滤观测的连续空间回归相结合，我们提出的方法随着时间的推移构建，更新和修正LTVM，推理环境中所有机器人部署的所有观测。我们目前的实验结果表明，从几个长期的机器人数据集提取的LTVM的准确性，鲁棒性和紧凑的性质。
英文摘要：
Autonomous service mobile robots need to consistently, accurately, and robustly localize in human environments despite changes to such environments over time. Episodic non-Markov Localization addresses the challenge of localization in such changing environments by classifying observations as arising from Long-Term, Short-Term, or Dynamic Features. However, in order to do so, EnML relies on an estimate of the Long-Term Vector Map (LTVM) that does not change over time. In this paper, we introduce a recursive algorithm to build and update the LTVM over time by reasoning about visibility constraints of objects observed over multiple robot deployments. We use a signed distance function (SDF) to filter out observations of short-term and dynamic features from multiple deployments of the robot. The remaining long-term observations are used to build a vector map by robust local linear regression. The uncertainty in the resulting LTVM is computed via Monte Carlo resampling the observations arising from long-term features. By combining occupancy-grid based SDF filtering of observations with continuous space regression of the filtered observations, our proposed approach builds, updates, and amends LTVMs over time, reasoning about all observations from all robot deployments in an environment. We present experimental results demonstrating the accuracy, robustness, and compact nature of the extracted LTVMs from several long-term robot datasets.
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