  Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been
presented in last decade using different sensor modalities. However, robust
SLAM in extreme weather conditions is still an open research problem. In this
paper, RadarSLAM, a full radar based graph SLAM system, is proposed for
reliable localization and mapping in large-scale environments. It is composed
of pose tracking, local mapping, loop closure detection and pose graph
optimization, enhanced by novel feature matching and probabilistic point cloud
generation on radar images. Extensive experiments are conducted on a public
radar dataset and several self-collected radar sequences, demonstrating the
state-of-the-art reliability and localization accuracy in various adverse
weather conditions, such as dark night, dense fog and heavy snowfall.
