SlideSLAM: Sparse, Lightweight, Decentralized Metric-Semantic SLAM for Multi-Robot Navigation
<|reference_start|>SlideSLAM: Sparse, Lightweight, Decentralized Metric-Semantic SLAM for Multi-Robot Navigation: This paper develops a real-time decentralized metric-semantic Simultaneous Localization and Mapping (SLAM) approach that leverages a sparse and lightweight object-based representation to enable a heterogeneous robot team to autonomously explore 3D environments featuring indoor, urban, and forested areas without relying on GPS. We use a hierarchical metric-semantic representation of the environment, including high-level sparse semantic maps of object models and low-level voxel maps. We leverage the informativeness and viewpoint invariance of the high-level semantic map to obtain an effective semantics-driven place-recognition algorithm for inter-robot loop closure detection across aerial and ground robots with different sensing modalities. A communication module is designed to track each robot's own observations and those of other robots whenever communication links are available. Such observations are then used to construct a merged map. Our framework enables real-time decentralized operations onboard robots, allowing them to opportunistically leverage communication. We integrate and deploy our proposed framework on three types of aerial and ground robots. Extensive experimental results show an average inter-robot localization error of approximately 20 cm in position and 0.2 degrees in orientation, an object mapping F1 score consistently over 0.9, and a communication packet size of merely 2-3 megabytes per kilometer trajectory with as many as 1,000 landmarks. The project website can be found at https://xurobotics.github.io/slideslam/.<|reference_end|>
@article{liu2024slideslam:,
    title={SlideSLAM: Sparse, Lightweight, Decentralized Metric-Semantic SLAM for
  Multi-Robot Navigation},
    author={Xu Liu, Jiuzhou Lei, Ankit Prabhu, Yuezhan Tao, Igor Spasojevic,
  Pratik Chaudhari, Nikolay Atanasov, Vijay Kumar},
    journal={arXiv preprint arXiv:2406.17249},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17249},
    primaryClass={cs.RO}
}