Fusion of Sequential Information forSemantic Grid Map Estimation
Frank Bieder, Muti Ur Rehman, Christoph Stiller
Kapitel/Beitrag aus dem Buch: Heizmann M. & Längle T. 2020. Forum Bildverarbeitung 2020.
Kapitel/Beitrag aus dem Buch: Heizmann M. & Längle T. 2020. Forum Bildverarbeitung 2020.
In this work, we improve the semantic segmentation
of multi-layer top-view grid maps in the context of LiDARbased
perception for autonomous vehicles. To achieve this
goal, we fuse sequential information from multiple consecutive
lidar measurements with respect to the driven trajectory
of an autonomous vehicle. By doing so, we enrich the multilayer
grid maps which are subsequently used as the input of
a neural network. Our approach can be used for LiDAR-only
360 surround view semantic scene segmentation while being
suitable for real-time critical systems. We evaluate the benefit
of fusing sequential information based on a dense ground
truth and discuss the effect on different semantic classes.
Bieder, F et al. 2020. Fusion of Sequential Information forSemantic Grid Map Estimation. In: Heizmann M. & Längle T (eds.), Forum Bildverarbeitung 2020. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.58895/ksp/1000124383-7
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Veröffentlicht am 25. November 2020