  Moving object segmentation (MOS) and Ego velocity estimation (EVE) are vital
capabilities for mobile systems to achieve full autonomy. Several approaches
have attempted to achieve MOSEVE using a LiDAR sensor. However, LiDAR sensors
are typically expensive and susceptible to adverse weather conditions. Instead,
millimeter-wave radar (MWR) has gained popularity in robotics and autonomous
driving for real applications due to its cost-effectiveness and resilience to
bad weather. Nonetheless, publicly available MOSEVE datasets and approaches
using radar data are limited. Some existing methods adopt point convolutional
networks from LiDAR-based approaches, ignoring the specific artifacts and the
valuable radial velocity information of radar measurements, leading to
suboptimal performance. In this paper, we propose a novel transformer network
that effectively addresses the sparsity and noise issues and leverages the
radial velocity measurements of radar points using our devised radar self- and
cross-attention mechanisms. Based on that, our method achieves accurate EVE of
the robot and performs MOS using only radar data simultaneously. To thoroughly
evaluate the MOSEVE performance of our method, we annotated the radar points in
the public View-of-Delft (VoD) dataset and additionally constructed a new radar
dataset in various environments. The experimental results demonstrate the
superiority of our approach over existing state-of-the-art methods. The code is
available at https://github.com/ORCA-Uboat/RadarMOSEVE.
