Ultra-Lightweight Collaborative Mapping for Robot Swarms

TL;DR

This paper presents a decentralized, lightweight collaborative SLAM method enabling small, low-cost robots and large swarms to perform accurate environment mapping onboard, with minimal hardware and communication requirements.

Contribution

It introduces a novel onboard, infrastructure-less collaborative SLAM approach suitable for tiny robots and large swarms, reducing hardware costs and computational demands significantly.

Findings

Achieved <30cm mapping accuracy on centimeter-size drones

Supported hundreds of agents with standard wireless protocols

Reduced hardware and computation costs by two orders of magnitude

Abstract

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.