Understanding while Exploring: Semantics-driven Active Mapping

2025-05-30

AI Summary

To address the challenge of autonomous robotic exploration in unknown environments—requiring simultaneous high-fidelity geometric reconstruction and robust semantic understanding—this paper proposes ActiveSGM, a novel active mapping framework. ActiveSGM introduces the first semantic uncertainty quantification mechanism built upon 3D Gaussian Splatting, tightly coupling sparse semantic encoding with geometric uncertainty modeling to enable joint semantic-geometric optimization. By predicting information gain from candidate viewpoints, it dynamically selects optimal observations, closing the “explore–perceive–map” loop. Experiments on Replica and Matterport3D demonstrate that ActiveSGM significantly improves mapping completeness (+18.7%), semantic segmentation accuracy (mIoU +12.3%), and robustness to sensor noise. The framework establishes a new paradigm for adaptive, open-world autonomous exploration.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Enhances robotic autonomy in unknown environments through proactive exploration
• Predicts informativeness of observations using semantic and geometric uncertainty
• Improves mapping completeness and accuracy with strategic viewpoint selection

Innovation

• Uses 3D Gaussian Splatting for mapping
• Quantifies semantic and geometric uncertainty
• Guides exploration with sparse semantic representation