Autonomous Exploration and Semantic Updating of Large-Scale Indoor Environments with Mobile Robots
<|reference_start|>Autonomous Exploration and Semantic Updating of Large-Scale Indoor Environments with Mobile Robots: We introduce a new robotic system that enables a mobile robot to autonomously explore an unknown environment, build a semantic map of the environment, and subsequently update the semantic map to reflect environment changes, such as location changes of objects. Our system leverages a LiDAR scanner for 2D occupancy grid mapping and an RGB-D camera for object perception. We introduce a semantic map representation that combines a 2D occupancy grid map for geometry, with a topological map for object semantics. This map representation enables us to effectively update the semantics by deleting or adding nodes to the topological map. Our system has been tested on a Fetch robot. The robot can semantically map a 93m x 90m floor and update the semantic map once objects are moved in the environment.<|reference_end|>
@article{allu2024autonomous,
    title={Autonomous Exploration and Semantic Updating of Large-Scale Indoor
  Environments with Mobile Robots},
    author={Sai Haneesh Allu, Itay Kadosh, Tyler Summers, Yu Xiang},
    journal={arXiv preprint arXiv:2409.15493},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15493},
    primaryClass={cs.RO cs.CV}
}