Ultra-Lightweight Collaborative Mapping for Robot Swarms
<|reference_start|>Ultra-Lightweight Collaborative Mapping for Robot Swarms: A key requirement in robotics is the ability to simultaneously self-localize and map a previously unknown environment, relying primarily on onboard sensing and computation. Achieving fully onboard accurate simultaneous localization and mapping (SLAM) is feasible for high-end robotic platforms, whereas small and inexpensive robots face challenges due to constrained hardware, therefore frequently resorting to external infrastructure for sensing and computation. The challenge is further exacerbated in swarms of robots, where coordination, scalability, and latency are crucial concerns. This work introduces a decentralized and lightweight collaborative SLAM approach that enables mapping on virtually any robot, even those equipped with low-cost hardware and only 1.5 MB of memory, including miniaturized insect-size devices. Moreover, the proposed solution supports large swarm formations with the capability to coordinate hundreds of agents. To substantiate our claims, we have successfully implemented collaborative SLAM on centimeter-size drones weighing 46 g. Remarkably, we achieve a mapping accuracy below 30 cm, a result comparable to high-end state-of-the-art solutions while reducing the cost, memory, and computation requirements by two orders of magnitude. Our approach is innovative in three main aspects. First, it enables onboard infrastructure-less collaborative mapping with a lightweight and cost-effective (\$20) solution in terms of sensing and computation. Second, we optimize the data traffic within the swarm to support hundreds of cooperative agents using standard wireless protocols such as ultra-wideband (UWB), Bluetooth, or WiFi. Last, we implement a distributed swarm coordination policy to decrease mapping latency and enhance accuracy.<|reference_end|>
@article{niculescu2024ultra-lightweight,
    title={Ultra-Lightweight Collaborative Mapping for Robot Swarms},
    author={Vlad Niculescu, Tommaso Polonelli, Michele Magno, Luca Benini},
    journal={arXiv preprint arXiv:2407.03136},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03136},
    primaryClass={cs.RO}
}