  A Simultaneous Localization and Mapping (SLAM) system must be robust to
support long-term mobile vehicle and robot applications. However, camera and
LiDAR based SLAM systems can be fragile when facing challenging illumination or
weather conditions which degrade their imagery and point cloud data. Radar,
whose operating electromagnetic spectrum is less affected by environmental
changes, is promising although its distinct sensing geometry and noise
characteristics bring open challenges when being exploited for SLAM. % However,
there are still open challenges since most existing visual and LiDAR SLAM
systems do not operate in bad weathers. This paper studies the use of a
Frequency Modulated Continuous Wave radar for SLAM in large-scale outdoor
environments. We propose a full radar SLAM system, including a novel radar
motion tracking algorithm that leverages radar geometry for reliable feature
tracking. It also optimally compensates motion distortion and estimates pose by
joint optimization. Its loop closure component is designed to be simple yet
efficient for radar imagery by capturing and exploiting structural information
of the surrounding environment. % while a scheme to reject ambiguous loop
closure candidates is also designed specifically for radar. Extensive
experiments on three public radar datasets, ranging from city streets and
residential areas to countryside and highways, show competitive accuracy and
reliability performance of the proposed radar SLAM system compared to the
state-of-the-art LiDAR, vision and radar methods. The results show that our
system is technically viable in achieving reliable SLAM in extreme weather
conditions, e.g. heavy snow and dense fog, demonstrating the promising
potential of using radar for all-weather localization and mapping.
