RT Conference Proceedings
T1 waveSLAM: Empowering accurate indoor mapping using off-the-shelf millimeter-wave self-sensing
A1 Picazo Martinez, Pablo
A1 Groshev, Milan
A1 Oliva Delgado, Antonio de la
AB 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.
YR 2023
FD 2023-11-20
LK https://hdl.handle.net/10016/38916
UL https://hdl.handle.net/10016/38916
LA eng
NO Proceedings of: 2023 IEEE 98th Vehicular Technology Conference: VTC2023-Fall, 10-13 October 2023, Hong Kong.
NO This work has been partially funded by the European Union's Horizon Europe research and innovation program under grant agreement No 101095759 (Hexa-X-II) and the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-Next Generation EU through the UNICO 5G I+D 6G-EDGEDT.
DS e-Archivo
RD 18 jul. 2024