A Modular Robotic System for Autonomous Exploration and Semantic Updating in Large-Scale Indoor Environments
This work addresses the challenge of enabling mobile robots to autonomously adapt to dynamic changes in large indoor spaces, representing an incremental advancement over prior methods that required manual intervention or precollected data.
The paper tackles the problem of autonomous exploration and semantic updating in large-scale indoor environments by developing a modular robotic system that builds, revisits, and updates a hybrid semantic map, validated on real-world environments up to 8,500 m² with robust and scalable performance.
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.