Alejo Teissière, DavidRey Arcenegui, RafaelCobano-Suárez, José-AntonioCaballero, FernandoMerino, Luis2025-03-122025-03-122022-11-22ROBOT2022: Fifth Iberian Robotics Conference. ROBOT 2022. Lecture Notes in Networks and Systems10.1007/978-3-031-21062-4_25https://hdl.handle.net/10433/23459This 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)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.application/pdfenData fusionRadarLow-visibilityLocalizationconference outputrestricted access