Monocular Semantic Occupancy Grid Mapping With Convolutional Variational Encoder-Decoder Networks

In this letter, we research and evaluate end-to-end learning of monocular semantic-metric occupancy grid mapping from weak binocular ground truth. The network learns to predict four classes, as well as a camera to bird's eye view mapping. At the core, it utilizes a variational encoder-decoder n...

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Vydané v:	IEEE robotics and automation letters Ročník 4; číslo 2; s. 445 - 452
Hlavní autori:	Chenyang Lu, van de Molengraft, Marinus Jacobus Gerardus, Dubbelman, Gijs
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.04.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Cameras Cartesian coordinates Coders computer vision for transportation Feature extraction Ground truth Image segmentation Learning Mapping Measurement Neural networks object detection Occupancy segmentation and categorization Semantic scene understanding Semantics Training
ISSN:	2377-3766, 2377-3766
On-line prístup:	Získať plný text
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