  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 on-board
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
on-board the UAV's low-power ARM Cortex-M microcontroller with just 192 kB of
SRAM memory. Field results gathered in three different mazes from 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 on-board
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.
