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Document type:
Zeitschriftenaufsatz
Author(s):
Nobis, Felix; Fent, Felix; Betz, Johannes; Lienkamp, Markus
Title:
Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation
Abstract:
State-of-the-art 3D object detection for autonomous driving is achieved by processing lidar sensor data with deep-learning methods. However, the detection quality of the state of the art is still far from enabling safe driving in all conditions. Additional sensor modalities need to be used to increase the confidence and robustness of the overall detection result. Researchers have recently explored radar data as an additional input source for universal 3D object detection. This paper proposes art...    »
Keywords:
FTM Fahrdynamik
Journal title:
Applied Sciences
Year:
2021
Journal volume:
11
Journal issue:
6
Pages contribution:
2599
Covered by:
Scopus
Language:
en
Fulltext / DOI:
doi:10.3390/app11062599
Publisher:
MDPI AG
E-ISSN:
2076-3417
Date of publication:
15.03.2021
TUM Institution:
Lehrstuhl für Fahrzeugtechnik
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