Abstract
The problem of instantaneous ego-motion estimation with mm-wave automotive radar is studied. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepEgo</i> , 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepEgo</i> . Additionally, a novel loss function, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Doppler loss</i> , 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.
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