4DEgo: ego-velocity estimation from high-resolution radar data

Frontiers in Signal Processing

Signal Processing

Reza Ghabcheloo¹  Prashant Kumar Rai¹  Nataliya Strokina² 

[1] Automation Technology and Mechanical Engineering, Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland;Computing Sciences, Faculty of Information Technology and Communication, Tampere University, Tampere, Finland;

关键词: ego-motion estimation;    4D automotive radar;    autonomous navigation;    transformers;    attention;   

DOI  :  10.3389/frsip.2023.1198205

 received in 2023-03-31, accepted in 2023-06-08,  发布年份 2023

来源: Frontiers

【 摘 要 】

Automotive radars allow for perception of the environment in adverse visibility and weather conditions. New high-resolution sensors have demonstrated potential for tasks beyond obstacle detection and velocity adjustment, such as mapping or target tracking. This paper proposes an end-to-end method for ego-velocity estimation based on radar scan registration. Our architecture includes a 3D convolution over all three channels of the heatmap, capturing features associated with motion, and an attention mechanism for selecting significant features for regression. To the best of our knowledge, this is the first work utilizing the full 3D radar heatmap for ego-velocity estimation. We verify the efficacy of our approach using the publicly available ColoRadar dataset and study the effect of architectural choices and distributional shifts on performance.

【 授权许可】

Unknown   
Copyright © 2023 Rai, Strokina and Ghabcheloo.