Among the abilities that autonomous mobile robots should exhibit, map building and localization are definitely recognized as fundamental. Consequently, countless algorithms for solving the Simultaneous Localization And Mapping (SLAM) problem have been proposed. Currently, their evaluation is performed ex-post, according to outcomes obtained when running the algorithms on data collected by robots in real or simulated environments. In this paper, we present a novel method that allows the ex-ante prediction of the performance of a SLAM algorithm in an unseen environment, before it is actually run. Our method collects the performance of a SLAM algorithm in a number of simulated environments, builds a model that represents the relationship between the observed performance and some geometrical features of the environments, and exploits such model to predict the performance of the algorithm in an unseen environment starting from its features.
翻译:自主移动机器人应展示、地图建设和本地化的能力中,自动移动机器人应展示、地图建设和本地化的能力无疑被认为是根本的。 因此,提出了无数解决同声本地化和绘图(SLAM)问题的算法。 目前,根据在实际或模拟环境中操作机器人所收集数据的算法时获得的结果,对这些算法进行了事后评估。 在本文中,我们提出了一个新颖的方法,在SLAM算法实际运行之前,可以事先预测该算法在无形环境中的性能。 我们的方法收集了在一系列模拟环境中的SLAM算法的性能,建立了一个模型,代表所观测到的性能与环境的某些几何特征之间的关系,并利用这种模型来预测从其特征开始的未知环境中的算法性。