  For safe and efficient operation, mobile robots need to perceive their
environment, and in particular, perform tasks such as obstacle detection,
localization, and mapping. Although robots are often equipped with microphones
and speakers, the audio modality is rarely used for these tasks. Compared to
the localization of sound sources, for which many practical solutions exist,
algorithms for active echolocation are less developed and often rely on
hardware requirements that are out of reach for small robots. We propose an
end-to-end pipeline for sound-based localization and mapping that is targeted
at, but not limited to, robots equipped with only simple buzzers and low-end
microphones. The method is model-based, runs in real time, and requires no
prior calibration or training. We successfully test the algorithm on the e-puck
robot with its integrated audio hardware, and on the Crazyflie drone, for which
we design a reproducible audio extension deck. We achieve centimeter-level wall
localization on both platforms when the robots are static during the
measurement process. Even in the more challenging setting of a flying drone, we
can successfully localize walls, which we demonstrate in a proof-of-concept
multi-wall localization and mapping demo.
