Fully Onboard SLAM for Distributed Mapping with a Swarm of Nano-Drones

TL;DR

This paper presents a fully onboard distributed SLAM system for a swarm of tiny nano-UAVs with limited payload and computing power, enabling autonomous mapping without external infrastructure.

Contribution

It introduces a novel onboard mapping framework for nano-UAV swarms that combines data fusion and SLAM algorithms within strict resource constraints.

Findings

Achieves 12 cm mapping accuracy in field tests.

Mapping time decreases as the number of UAVs increases.

Supports communication and mapping for up to 20 nano-UAVs over 180 m2.

Abstract

The use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in applications ranging from surveillance and first-aid missions to industrial automation involving cooperation with other machines or humans. To maximize area coverage and reduce mission latency, swarms of collaborating drones have become a significant research direction. However, this approach requires open challenges in positioning, mapping, and communications to be addressed. This work describes a distributed mapping system based on a swarm of nano-UAVs, characterized by a limited payload of 35 g and tightly constrained onboard sensing and computing capabilities. Each nano-UAV is equipped with four 64-pixel depth sensors that measure the relative distance to obstacles in four directions. The proposed system merges the information from the swarm and generates a coherent grid map without relying on any external infrastructure. The data fusion is performed using the iterative closest point algorithm and a graph-based simultaneous localization and mapping algorithm, running entirely onboard the UAV's low-power ARM Cortex-M microcontroller with just 192 kB of memory. Field results gathered in three different mazes with a swarm of up to 4 nano-UAVs prove a mapping accuracy of 12 cm and demonstrate that the mapping time is inversely proportional to the number of agents. The proposed framework scales linearly in terms of communication bandwidth and onboard computational complexity, supporting communication between up to 20 nano-UAVs and mapping of areas up to 180 m2 with the chosen configuration requiring only 50 kB of memory.