Radarize: Large-Scale Radar SLAM for Indoor Environments
Emerson Sie Xinyu Wu Heyu Guo Deepak Vasisht
We present Radarize, a self-contained SLAM pipeline for indoor environments
that uses only a low-cost commodity single-chip mmWave radar. Our radar-native
approach leverages phenomena unique to radio frequencies, such as doppler
shift-based odometry, to improve performance. We evaluate our method on a
large-scale dataset of 146 trajectories spanning 4 campus buildings, totaling
approximately 4680m of travel distance. Our results show that our method
outperforms state-of-the-art radar-based 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 additional sensors such as IMUs or
wheel odometry.