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

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 four 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.

I. Introduction

Unmanned aerial vehicles (UAVs) have emerged as attractive solutions for several applications that require high maneuverability and scalability, such as distributed inspection and surveillance [1]. In particular, as defined in [2], nano-UAVs have proven to be safe to operate near people due to their reduced weight, i.e., below 50 g, which makes them an excellent choice for navigating through indoor or cramped environments [3]. Furthermore, they are agile, small enough to fit in the palm of a hand, and their cost-effective hardware facilitates swarm formations. An Internet of Robotic Things (IoRT) swarm [4] allows for a decreased latency and a higher probability of reaching the mission objective due to the intrinsic redundancy of having more than one drone [5]. Finding gas leaks [6], localizing survivors in mines [7], remote health monitoring of COVID-19 patients [4], or machine-to-machine cooperation with the Internet of Things (IoT) devices [8] are only a few examples where nano-UAVs and IoRT [9], with a single agent or a swarm, can be employed. Within such applications, UAVs have to perceive the environment and compute their next movements to enable optimal mission strategy [10]. However, enabling optimal planning requires good knowledge of the map of the environment as demonstrated in [10].