Radar-PointGNN: Graph Based Object Recognition for Unstructured Radar Point-cloud Data

Peter Svenningsson, Francesco Fioranelli, Alexander Yarovoy
2021 2021 IEEE Radar Conference (RadarConf21)  
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
more » ... onvolutional 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-toend deep learning solutions for object recognition in the radar domain are viable but currently not competitive with solutions based on LiDAR data.
doi:10.1109/radarconf2147009.2021.9455172 fatcat:6thxt5wmezfqvcazqoq7i652zq