CREVE: An Acceleration-based Constraint Approach for Robust Radar Ego-Velocity Estimation
<|reference_start|>CREVE: An Acceleration-based Constraint Approach for Robust Radar Ego-Velocity Estimation: Ego-velocity estimation from point cloud measurements of a millimeter-wave frequency-modulated continuous wave (mmWave FMCW) radar has become a crucial component of radar-inertial odometry (RIO) systems. Conventional approaches often perform poorly when the number of point cloud outliers exceeds that of inliers. In this paper, we propose CREVE, an acceleration-based inequality constraints filter that leverages additional measurements from an inertial measurement unit (IMU) to achieve robust ego-velocity estimations. To further enhance accuracy and robustness against sensor errors, we introduce a practical accelerometer bias estimation method and a parameter adaptation rule. The effectiveness of the proposed method is evaluated using five open-source drone datasets. Experimental results demonstrate that our algorithm significantly outperforms three existing state-of-the-art methods, achieving reductions in absolute trajectory error of approximately 53%, 84%, and 35% compared to them.<|reference_end|>
@article{do2024creve:,
    title={CREVE: An Acceleration-based Constraint Approach for Robust Radar
  Ego-Velocity Estimation},
    author={Hoang Viet Do, Bo Sung Ko, and Jin Woo Song},
    journal={arXiv preprint arXiv:2409.16847},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16847},
    primaryClass={cs.RO}
}