Picazo Martinez, PabloGroshev, MilanOliva Delgado, Antonio de la2023-11-202023-11-202023-11-20Picazo, P, Groshev, M., Blanco, A., Fiandrino, C., de la Oliva, A. & Widmer, J. (10-13 October 2023). waveSLAM: empowering accurate indoor mapping using off-the-shelf millimeter-wave self-sensing [proceedings]. 2023 IEEE 98th Vehicular Technology Conference: VTC2023-Fall, Hong Kong.https://hdl.handle.net/10016/38916Proceedings of: 2023 IEEE 98th Vehicular Technology Conference: VTC2023-Fall, 10-13 October 2023, Hong Kong.This paper presents the design, implementation and evaluation of waveSLAM, a low-cost mobile robot system that uses the millimetre wave (mmWave) communication devices to enhance the indoor mapping process targeting environments with reduced visibility or glass/mirror walls. A unique feature of waveSLAM is that it only leverages existing Commercial-Off-The-Shelf (COTS) hardware (Lidar and mmWave radios) that are mounted on mobile robots to improve the accurate indoor mapping achieved with optical sensors. The key intuition behind the waveSLAM design is that while the mobile robots moves freely, the mmWave radios can periodically exchange angle and distance estimates between themselves (self-sensing) by bouncing the signal from the environment, thus enabling accurate estimates of the target object/material surface. Our experiments verify that waveSLAM can archive cm-level accuracy with errors below 22 cm and 20◦ in angle orientation which is compatible with Lidar when building indoor maps.7eng© 2023 IEEE.Indoor mappingMmWave sensing60 GHzSelfsensingBeamsweepingFTMCSIconference posterTelecomunicacionesopen access17CC/0000034559