Semantic Grid Estimation with Occupancy Grids and Semantic Segmentation Networks

Creative Commons License

ERKENT Ö., Wolf C., Laugier C.

15th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore, Singapur, 18 - 21 Kasım 2018, ss.1051-1056 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/icarcv.2018.8581180
  • Basıldığı Şehir: Singapore
  • Basıldığı Ülke: Singapur
  • Sayfa Sayıları: ss.1051-1056
  • Hacettepe Üniversitesi Adresli: Hayır

Özet

We propose a method to estimate the semantic grid for an autonomous vehicle. The semantic grid is a 2D bird's eye view map where the grid cells contain semantic characteristics such as road, car, pedestrian, signage, etc. We obtain the semantic grid by fusing the semantic segmentation information and an occupancy grid computed by using a Bayesian filter technique. To compute the semantic information from a monocular RGB image, we integrate segmentation deep neural networks into our model. We use a deep neural network to learn the relation between the semantic information and the occupancy grid which can be trained end-to-end extending our previous work on semantic grids. Furthermore, we investigate the effect of using a conditional random field to refine the results. Finally, we test our method on two datasets and compare different architecture types for semantic segmentation. We perform the experiments on KITTI dataset and Inria-Chroma dataset.