RadarMOSEVE: A Spatial-Temporal Transformer Network for Radar-Only Moving Object Segmentation and Ego-Velocity Estimation
<|reference_start|>RadarMOSEVE: A Spatial-Temporal Transformer Network for Radar-Only Moving Object Segmentation and Ego-Velocity Estimation: 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.<|reference_end|>
@article{pang2024radarmoseve:,
    title={RadarMOSEVE: A Spatial-Temporal Transformer Network for Radar-Only
  Moving Object Segmentation and Ego-Velocity Estimation},
    author={Changsong Pang, Xieyuanli Chen, Yimin Liu, Huimin Lu, Yuwei Cheng},
    journal={Proceedings of the AAAI Conference on Artificial
  Intelligence.38(2024)4424-4432},
    year={2024},
    doi={10.1609/aaai.v38i5.28240},
    archivePrefix={arXiv},
    eprint={2402.14380},
    primaryClass={cs.RO}
}