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Fusion of Sequential Information for Semantic Grid Map Estimation

Bieder, Frank; Rehman, Muti Ur; Stiller, Christoph

Abstract (englisch):
In this work, we improve the semantic segmentationof multi-layer top-view grid maps in the context of LiDAR-based perception for autonomous vehicles. To achieve thisgoal, we fuse sequential information from multiple consecu-tive lidar measurements with respect to the driven trajectoryof an autonomous vehicle. By doing so, we enrich the multi-layer grid maps which are subsequently used as the input ofa neural network. Our approach can be used for LiDAR-only360◦surround view semantic scene segmentation while beingsuitable for real-time critical systems. We evaluate the bene-fit of fusing sequential information based on a dense groundtruth and discuss the effect on different semantic classes.

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Verlagsausgabe §
DOI: 10.5445/IR/1000129192
Veröffentlicht am 01.02.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Mess- und Regelungstechnik mit Maschinenlaboratorium (MRT)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 11.2020
Sprache Englisch
Identifikator ISBN: 978-3-7315-1053-6
KITopen-ID: 1000129192
Erschienen in Forum Bildverarbeitung 2020. Ed.: T. Längle ; M. Heizmann
Verlag KIT Scientific Publishing
Seiten 79-89
Schlagwörter Autonomous driving, sensor data fusion, semanticgrid map estimation
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