Grid maps, especially occupancy grid maps, are ubiquitous in many mobile robot applications. To simplify the process of learning the map, grid maps subdivide the world into a grid of cells, whose occupancies are independently estimated using only measurements in the perceptual field of the particular cell. However, the world consists of objects that span multiple cells, which means that measurements falling onto a cell provide evidence on the occupancy of other cells belonging to the same object. This correlation is not captured by current models. In this work, we present a way to generalize the update of grid maps relaxing the assumption of independence by modeling the relationship between the measurements and the occupancy of each cell as a set of latent variables, and jointly estimating those variables and the posterior of the map. Additionally, we propose a method to estimate the latent variables by clustering based on semantic labels and an extension to the Normal Distributions Transfer Occupancy Map (NDT-OM) to facilitate the proposed map update method. We perform comprehensive experiments of map creation and localization with real world data sets, and show that the proposed method creates better maps in highly dynamic environments compared to state-of-the-art methods. Finally, we demonstrate the ability of the proposed method to remove occluded objects from the map in a lifelong map update scenario.

翻译：栅格地图，特别是占据栅格地图，在众多移动机器人应用中普遍存在。为简化地图学习过程，栅格地图将世界细分为规则的栅格单元，每个单元的占据状态仅基于其感知场内的测量值独立估计。然而，真实世界由跨越多个栅格单元的目标构成，这意味着落在某个单元上的测量值能为同一目标其他单元的占据状态提供证据——这种相关性未被现有模型捕捉。本研究提出一种泛化栅格地图更新方法，通过将测量值与每个单元占据状态间的关系建模为隐变量集合，并联合估计这些变量与地图后验概率，从而放宽独立性假设。此外，我们提出基于语义标签聚类的隐变量估计方法，并对正态分布变换占据地图(NDT-OM)进行扩展以支持所提地图更新方法。我们基于真实世界数据集开展了地图构建与定位的综合实验，结果表明所提方法在高度动态环境中能生成优于现有技术的地图。最后，我们展示了所提方法在终身地图更新场景中移除被遮挡目标的能力。