A Modular Robotic System for Autonomous Exploration and Semantic Updating in Large-Scale Indoor Environments

TL;DR

This paper introduces a modular autonomous robotic system capable of exploring large indoor environments, building and updating semantic maps in real-time, and adapting to environmental changes without manual intervention.

Contribution

It presents a novel end-to-end autonomous exploration and semantic updating system that integrates geometric and semantic mapping with dynamic environment adaptation.

Findings

Successfully tested on a Fetch robot in real-world environments

Achieved robust and scalable semantic mapping

Demonstrated continuous environment adaptation

Abstract

We present a modular robotic system for autonomous exploration and semantic updating of large-scale unknown environments. Our approach enables a mobile robot to build, revisit, and update a hybrid semantic map that integrates a 2D occupancy grid for geometry with a topological graph for object semantics. Unlike prior methods that rely on manual teleoperation or precollected datasets, our two-phase approach achieves end-to-end autonomy: first, a modified frontier-based exploration algorithm with dynamic search windows constructs a geometric map; second, using a greedy trajectory planner, environments are revisited, and object semantics are updated using open-vocabulary object detection and segmentation. This modular system, compatible with any metric SLAM framework, supports continuous operation by efficiently updating the semantic graph to reflect short-term and long-term changes such as object relocation, removal, or addition. We validate the approach on a Fetch robot in real-world indoor environments of approximately $8,500$m$^2$ and $117$m$^2$, demonstrating robust and scalable semantic mapping and continuous adaptation, marking a fully autonomous integration of exploration, mapping, and semantic updating on a physical robot.