Road Lane Semantic Segmentation for High Definition Map
| Autor: | Euntai Kim, Jhonghyun An, Myungki Sun, Minho Cho, Sang-Yun Lee, Wonje Jang |
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| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Image segmentation Simultaneous localization and mapping Semantics Image (mathematics) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) High definition 020201 artificial intelligence & image processing Segmentation Computer vision Artificial intelligence business |
| Zdroj: | Intelligent Vehicles Symposium |
| DOI: | 10.1109/ivs.2018.8500661 |
| Popis: | High Definition map (HD Map) is an important part of autonomous driving vehicle. Most conventional method to generate HD map requires expensive system and postprocessing of observed data. In this paper, we propose automatic HD map generating algorithm using just monocular camera without further human labors. The proposed algorithm detects road lane from image and classifies the type of road lane at pixel-level with Fully Convolutional Network (FCN) which outperforms the other semantic segmentation methods. The segmentation results are used to extract lane features, and the features are used for loop-closure detection. Final map is generated with graph-based Simultaneous Localization and Mapping (SLAM) algorithm. The experiment is done with monocular camera mounted on mobile vehicle. In this paper, final map generated by proposed method is compared with aerial view data. The results show that the proposed method can generate reliable map that is comparable to real roads even only the low-cost sensor is used. |
| Databáze: | OpenAIRE |
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