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 frame- work, 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 sq.m and 117 sq.m, demonstrating robust and scalable semantic mapping and continuous adaptation, marking a fully autonomous integration of exploration, mapping, and semantic updating on a physical robot.

@misc{allu2025modularroboticautonomousexploration,
        title={A Modular Robotic System for Autonomous Exploration and Semantic Updating in Large-Scale Indoor Environments}, 
        author={Sai Haneesh Allu and Itay Kadosh and Tyler Summers and Yu Xiang},
        year={2025},
        eprint={2409.15493},
        archivePrefix={arXiv},
        primaryClass={cs.RO},
        url={https://arxiv.org/abs/2409.15493}
}