  Millimeter-wave (mmWave) radar is increasingly being considered as an
alternative to optical sensors for robotic primitives like simultaneous
localization and mapping (SLAM). While mmWave radar overcomes some limitations
of optical sensors, such as occlusions, poor lighting conditions, and privacy
concerns, it also faces unique challenges, such as missed obstacles due to
specular reflections or fake objects due to multipath. To address these
challenges, we propose Radarize, a self-contained SLAM pipeline that uses only
a commodity single-chip mmWave radar. Our radar-native approach uses techniques
such as Doppler shift-based odometry and multipath artifact suppression to
improve performance. We evaluate our method on a large dataset of 146
trajectories spanning 4 buildings and mounted on 3 different platforms,
totaling approximately 4.7 Km of travel distance. Our results show that our
method outperforms state-of-the-art radar and radar-inertial approaches by
approximately 5x in terms of odometry and 8x in terms of end-to-end SLAM, as
measured by absolute trajectory error (ATE), without the need for additional
sensors such as IMUs or wheel encoders.
