End-to-End Learning of Semantic Grid Estimation Deep Neural Network with Occupancy Grids

Author:

Erkent Özgür1ORCID,Wolf Christian123,Laugier Christian1

Affiliation:

1. INRIA, Chroma Team, Rhône-Alpes, France

2. Université de Lyon, INSA-Lyon, CNRS, LIRIS, F-69621, France

3. CITI, INSA-Lyon, F-69621, France

Abstract

We propose semantic grid, a spatial 2D map of the environment around an autonomous vehicle consisting of cells which represent the semantic information of the corresponding region such as car, road, vegetation, bikes, etc. It consists of an integration of an occupancy grid, which computes the grid states with a Bayesian filter approach, and semantic segmentation information from monocular RGB images, which is obtained with a deep neural network. The network fuses the information and can be trained in an end-to-end manner. The output of the neural network is refined with a conditional random field. The proposed method is tested in various datasets (KITTI dataset, Inria-Chroma dataset and SYNTHIA) and different deep neural network architectures are compared.

Funder

Toyota Motor Europe

Publisher

World Scientific Pub Co Pte Lt

Subject

Control and Optimization,Aerospace Engineering,Automotive Engineering,Control and Systems Engineering

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