🤖 AI Summary
When outliers outnumber inliers in millimeter-wave FMCW radar point clouds, conventional self-velocity estimation fails, severely degrading Radar-Inertial Odometry (RIO) navigation performance. To address this, we propose a robust acceleration inequality-constrained filtering framework. Methodologically, we formulate a tightly coupled IMU-radar state model and incorporate physically grounded acceleration inequality constraints to suppress outlier-induced errors. We further design an online accelerometer bias estimation algorithm and an adaptive parameter tuning mechanism to enhance real-time performance and robustness. Extensive experiments on five open-source UAV datasets demonstrate that our method significantly outperforms three state-of-the-art approaches, achieving an average 57% reduction in absolute trajectory error. This validates its effectiveness and superiority in high-outlier-rate scenarios.
📝 Abstract
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.