Traditional approaches for active mapping focus on building geometric maps. For most real-world applications, however, actionable information is related to semantically meaningful objects in the environment. We propose an approach to the active metric-semantic mapping problem that enables multiple heterogeneous robots to collaboratively build a map of the environment. The robots actively explore to minimize the uncertainties in both semantic (object classification) and geometric (object modeling) information. We represent the environment using informative but sparse object models, each consisting of a basic shape and a semantic class label, and characterize uncertainties empirically using a large amount of real-world data. Given a prior map, we use this model to select actions for each robot to minimize uncertainties. The performance of our algorithm is demonstrated through multi-robot experiments in diverse real-world environments. The proposed framework is applicable to a wide range of real-world problems, such as precision agriculture, infrastructure inspection, and asset mapping in factories. A demo video can be found at https://youtu.be/S86SgXi54oU.

翻译：积极绘图的传统方法侧重于建立几何地图。然而,对于大多数现实世界应用,可操作的信息与环境中具有地震意义的物体有关。我们建议了一种方法,解决主动的超语系绘图问题,使多个多异机器人能够合作绘制环境地图。机器人积极探索如何尽量减少语系(物体分类)和几何(物体建模)信息的不确定性。我们使用信息丰富但稀少的物体模型代表环境,每个模型都包含基本形状和语系类标签,并用大量真实世界数据对不确定性进行实证定性。根据先前的地图,我们使用这一模型为每个机器人选择行动,以尽量减少不确定性。我们算法的性能通过不同现实世界环境中的多机器人实验得到证明。拟议框架适用于一系列广泛的现实世界问题,如精确农业、基础设施检查和工厂的资产测绘。可在https://youtu.be/S86SgXi54oU上看到一个演示视频。