Active Metric-Semantic Mapping by Multiple Aerial Robots

TL;DR

This paper presents a multi-robot system for active metric-semantic mapping that collaboratively explores environments to reduce uncertainties in object classification and modeling, applicable to various real-world scenarios.

Contribution

It introduces a novel approach enabling multiple heterogeneous robots to actively explore and build semantic maps with uncertainty minimization, validated through real-world experiments.

Findings

Effective multi-robot exploration reduces semantic and geometric uncertainties.

The approach is applicable to diverse environments like agriculture and infrastructure.

Experimental results demonstrate improved mapping accuracy and efficiency.

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 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.