Perception systems for autonomous vehicles are reliant on a comprehensive sensor suite to identify objects in the environment. While object recognition systems in the LiDAR and camera modalities are reaching maturity, recognition models on sparse radar point measurements have remained an open research challenge. An object recognition model is here presented which imposes a graph structure on the radar point-cloud by connecting spatially proximal points and extracts local patterns by performing convolutional operations across the graph’s edges. The model’s performance is evaluated by the nuScenes benchmark and is the first radar object recognition model evaluated on the dataset. The results show that end-to-end deep learning solutions for object recognition in the radar domain are viable but currently not competitive with solutions based on LiDAR data.
|Title of host publication||2021 IEEE Radar Conference|
|Subtitle of host publication||Radar on the Move, RadarConf 2021|
|Number of pages||6|
|Publication status||Published - 2021|
|Event||2021 IEEE Radar Conference (RadarConf21): Radar on the Move - Atlanta, United States|
Duration: 7 May 2021 → 14 May 2021
|Name||IEEE National Radar Conference - Proceedings|
|Conference||2021 IEEE Radar Conference (RadarConf21)|
|Period||7/05/21 → 14/05/21|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- object detection
- object recognition
- geo-metric deep learning
- geometric deep learning