Active Metric-Semantic Mapping by Multiple Aerial Robots

Abstract

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 multirobot experiments in diverse real-world environments. The proposed framework is applicable to a wide range of realworld problems, such as precision agriculture, infrastructure inspection, and asset mapping in factories.

Graph neural networks for decentralized multi-agent perimeter defense

“…In this work, we tackle the perimeter defense problem in a domain where multiple agents collaborate to accomplish a task. Multi-agent collaboration has been explored in many areas including environmental mapping (Thrun et al, 2000;Liu et al, 2022), search and rescue (Baxter et al, 2007;Miller et al, 2020), target tracking (Lee et al, 2022b;Ge et al, 2022), ondemand wireless infrastructure (Mox et al, 2020), transportation (Ng et al, 2022;Xu et al, 2022), and multi-agent learning (Kim et al, 2021). Our approach employs a team of robots that work collectively towards a common goal of defending a perimeter.…”