  Consistent motion estimation is fundamental for all mobile autonomous
systems. While this sounds like an easy task, often, it is not the case because
of changing environmental conditions affecting odometry obtained from vision,
Lidar, or the wheels themselves. Unsusceptible to challenging lighting and
weather conditions, radar sensors are an obvious alternative. Usually,
automotive radars return a sparse point cloud, representing the surroundings.
Utilizing this information to motion estimation is challenging due to unstable
and phantom measurements, which result in a high rate of outliers. We introduce
a credible and robust probabilistic approach to estimate the ego-motion based
on these challenging radar measurements; intended to be used within a
loosely-coupled sensor fusion framework. Compared to existing solutions,
evaluated on the popular nuScenes dataset and others, we show that our proposed
algorithm is more credible while not depending on explicit correspondence
calculation.
