  Motivated by lunar exploration, we consider deploying a network of mobile
robots to explore an unknown environment while acting as a cooperative
positioning system. Robots measure and communicate position-related data in
order to perform localization in the absence of infrastructure-based solutions
(e.g. stationary beacons or GPS). We present Trilateration for Exploration and
Mapping (TEAM), a novel algorithm for low-complexity localization and mapping
with robotic networks. TEAM is designed to leverage the capability of
commercially-available ultra-wideband (UWB) radios on board the robots to
provide range estimates with centimeter accuracy and perform anchorless
localization in a shared, stationary frame. It is well-suited for
feature-deprived environments, where feature-based localization approaches
suffer. We provide experimental results in varied Gazebo simulation
environments as well as on a testbed of Turtlebot3 Burgers with Pozyx UWB
radios. We compare TEAM to the popular Rao-Blackwellized Particle Filter for
Simultaneous Localization and Mapping (SLAM). We demonstrate that TEAM requires
an order of magnitude less computational complexity and reduces the necessary
sample rate of LiDAR measurements by an order of magnitude. These advantages do
not require sacrificing performance, as TEAM reduces the maximum localization
error by 50% and achieves up to a 28% increase in map accuracy in
feature-deprived environments and comparable map accuracy in other settings.
