RT Conference Proceedings
T1 Data fusion of RADAR and LIDAR for robot localization under low-visibility conditions in structured environments
A1 Alejo Teissière, David
A1 Rey Arcenegui, Rafael
A1 Cobano-Suárez, José-Antonio
A1 Caballero, Fernando
A1 Merino, Luis
K1 Data fusion
K1 Radar
K1 Low-visibility
K1 Localization
AB Optical range sensors such as LiDAR and range cameras have become the most common devices for robot localization and navigation tasks. However, their performance can be degraded by meteorological hazards, such as fog, smoke, or rain. This paper proposes a new method to combine information from LiDAR sensors and low-cost RADAR sensors in structured 2D environments, in order to ensure the availability of useful information in low-visibility conditions due to smoke. Our method makes use of a novel DBScan-Line segmentation for clustering the measurements from the LiDAR sensor, and then it establishes correspondences between these  clusters and the measurements from the RADAR sensors. The method has been extensively tested in field experiments with artificial smoke, and the results benchmarked against raw sensors and a state-of-the-art fusion method. Moreover, the fused measurements have been integrated into a localization method, which was able to robustly localize a ground platform in the presence of dense fog.
PB Springer
YR 2022
FD 2022-11-22
LK https://hdl.handle.net/10433/23459
UL https://hdl.handle.net/10433/23459
LA en
NO ROBOT2022: Fifth Iberian Robotics Conference. ROBOT 2022. Lecture Notes in Networks and Systems
NO This work is partially supported by the Spanish Ministry of Science, Innovation and Universities (COMCISE RTI2018-100847-B-C22, MCIU/AEI/FEDER, UE) and by Programa Operativo FEDER Andalucia 2014-2020 through the project DeepBot (PY20 00817).Proyecto de Investigación: COMCISE RTI2018-100847-B-C22, MCIU/AEI/FEDER, UE y proyecto DeepBot (PY20 00817)
NO Departamento de Deporte e Informática
DS RIO
RD Apr 22, 2026