DeepEgo
Deep Instantaneous Ego-Motion Estimation Using Automotive Radar
Simin Zhu (TU Delft - Microwave Sensing, Signals & Systems)
A.G. Yarovoy (TU Delft - Microwave Sensing, Signals & Systems)
Francesco Fioranelli (TU Delft - Microwave Sensing, Signals & Systems)
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Abstract
The problem of instantaneous ego-motion estimation with mm-wave automotive radar is studied. DeepEgo, a deep learning-based method, is proposed for achieving robust and accurate ego-motion estimation. A hybrid approach that uses neural networks to extract complex features from input point clouds and applies weighted least squares (WLS) for motion estimation is utilized in DeepEgo. Additionally, a novel loss function, Doppler loss, is proposed to locate “inlier points” originating from detected stationary objects without human annotation. Finally, a challenging real-world automotive radar dataset is selected for extensive performance evaluation. Compared to other methods selected from the literature, significant improvements in estimation accuracy, long-term stability, and runtime performance of DeepEgo in comparison to other methods are demonstrated.