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Semantic Grid Estimation with Occupancy Grids and Semantic Segmentation Networks

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Abstract

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.
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Dates and versions

hal-01933939 , version 1 (24-11-2018)

Identifiers

  • HAL Id : hal-01933939 , version 1

Cite

Özgür Erkent, Christian Wolf, Christian Laugier. Semantic Grid Estimation with Occupancy Grids and Semantic Segmentation Networks. ICARCV 2018 - 15th International Conference on Control, Automation, Robotics and Vision, Nov 2018, Singapore, Singapore. pp.1-6. ⟨hal-01933939⟩
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