Abstract
With deep learning based perception tasks on radar input data gaining more attention for autonomous driving, the use of new data interfaces, specifically range-beam-doppler tensors, are explored to maximize the performance of corresponding algorithms. Surprisingly, in past publications, the Doppler information of this data has only played a minor role, even though velocity is considered a powerful feature. We investigate the hypothesis that the sensor ego-velocity, induced by the ego vehicle motion, increases the data generalization complexity of the range-beam-doppler data and propose a phase shift of the electromagnetic wave to normalize the data by compensating for the ego vehicle motion. We show its efficacy versus non-compensated data with an improvement of 8.7% average precision (AP) for object detection tasks.
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Meyer, M., Unzueta, M., Kuschk, G., Tomforde, S. (2023). Ego-Motion Compensation of Range-Beam-Doppler Radar Data for Object Detection. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13801. Springer, Cham. https://doi.org/10.1007/978-3-031-25056-9_44
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