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, 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 only 46 grams. Remarkably, we achieve results 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 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.

翻译：机器人学中的一个关键要求是能够主要依赖机载传感与计算，在未知环境中同时实现自主定位与地图构建。对于高端机器人平台而言，实现完全机载的精确同步定位与建图（SLAM）是可行的；然而，小型且廉价的机器人由于硬件受限而面临挑战，因此常常需要依赖外部基础设施进行传感与计算。在机器人集群中，这一挑战进一步加剧，因为协调性、可扩展性与延迟成为至关重要的考量因素。本研究提出了一种去中心化、轻量级的协同SLAM方法，使得几乎任何机器人——即使是配备低成本硬件的设备，包括微型昆虫尺寸的装置——都能实现建图。此外，所提出的解决方案支持大规模集群编队，具备协调数百个智能体的能力。为证实我们的主张，我们已成功在仅重46克的厘米级无人机上实现了协同SLAM。值得注意的是，我们在将成本、内存和计算需求降低两个数量级的同时，获得了与高端先进解决方案相媲美的结果。我们的方法在三个主要方面具有创新性：首先，它通过轻量且成本效益高的传感与计算方案，实现了无需外部基础设施的机载协同建图；其次，我们优化了集群内部的数据传输，利用超宽带（UWB）、蓝牙或WiFi等标准无线协议支持数百个协作智能体；最后，我们实施了一种分布式集群协调策略，以降低建图延迟并提升精度。