  In this letter, we propose MAROAM, a millimeter wave radar-based SLAM
framework, which employs a two-step feature selection process to build the
global consistent map. Specifically, we first extract feature points from raw
data based on their local geometric properties to filter out those points that
violate the principle of millimeter-wave radar imaging. Then, we further employ
another round of probabilistic feature selection by examining how often and how
recent the feature point has been detected in the proceeding frames. With such
a two-step feature selection, we establish a global consistent map for accurate
and robust pose estimation as well as other downstream tasks. At last, we
perform loop closure and graph optimization in the back-end, further reducing
the accumulated drift error.
  We evaluate the performance of MAROAM on the three datasets: the Oxford Radar
RobotCar Dataset, the MulRan Dataset and the Boreas Dataset. We consider a
variety of experimental settings with different scenery, weather, and road
conditions. The experimental results show that the accuracy of MAROAM is 7.95%,
37.0% and 8.9% higher than the currently best-performing algorithms on these
three datasets, respectively. The ablation results also show that our map-based
odometry performs 28.6% better than the commonly used scan-to-frames method.
Finally, as devoted contributors to the open-source community, we will open
source the algorithm after the paper is accepted.
