Understanding while Exploring: Semantics-driven Active Mapping

TL;DR

This paper introduces ActiveSGM, an active semantic mapping framework that predicts observation informativeness to guide robotic exploration, improving scene understanding and robustness in unknown environments.

Contribution

The paper presents a novel active semantic mapping approach using 3D Gaussian Splatting that predicts observation value to optimize exploration strategies.

Findings

Enhanced mapping completeness and accuracy

Robustness to noisy semantic data

Effective exploration on Replica and Matterport3D datasets

Abstract

Effective robotic autonomy in unknown environments demands proactive exploration and precise understanding of both geometry and semantics. In this paper, we propose ActiveSGM, an active semantic mapping framework designed to predict the informativeness of potential observations before execution. Built upon a 3D Gaussian Splatting (3DGS) mapping backbone, our approach employs semantic and geometric uncertainty quantification, coupled with a sparse semantic representation, to guide exploration. By enabling robots to strategically select the most beneficial viewpoints, ActiveSGM efficiently enhances mapping completeness, accuracy, and robustness to noisy semantic data, ultimately supporting more adaptive scene exploration. Our experiments on the Replica and Matterport3D datasets highlight the effectiveness of ActiveSGM in active semantic mapping tasks.