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

Conference paper (2021)

Authors

P.O. Svenningsson Microwave Sensing, Signals & Systems - EEMCS
F. Fioranelli Microwave Sensing, Signals & Systems - EEMCS
Alexander Yarovoy Microwave Sensing, Signals & Systems - EEMCS
Research Group
Microwave Sensing, Signals & Systems (EEMCS) (TU Delft)
Copyright: © 2021 P.O. Svenningsson, F. Fioranelli, Alexander Yarovoy
DOI: https://doi.org/10.1109/RadarConf2147009.2021.9455172
Radar Object detection Object recognition NuScenes Geometric deep learning Geo-metric deep learning
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Microwave Sensing, Signals & Systems

Abstract

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.

Files

09455172.pdf
(.pdf | 4.67 Mb)