Semantic Grid Estimation with a Hybrid Bayesian and Deep Neural Network Approach

In an autonomous vehicle setting, we propose a method for the estimation of a semantic grid, i.e. a bird's eye grid centered on the car's position and aligned with its driving direction, which contains high-level semantic information about the environment and its actors. Each grid cell contains a semantic label with divers classes, as for instance {Road, Vegetation, Building, Pedestrian, Car...}. We propose a hybrid approach, which combines the advantages of two different methodologies: we use Deep Learning to perform semantic segmentation on monocular RGB images with supervised learning from labeled groundtruth data. We combine these segmentations with occupancy grids calculated from LIDAR data using a generative Bayesian particle filter. The fusion itself is carried out with a deep neural network, which learns to integrate geometric information from the LIDAR with semantic information from the RGB data. We tested our method on two datasets, namely the KITTI dataset, which is publicly available and widely used, and our own dataset obtained with our own platform, equipped with a LIDAR and various sensors. We largely outperform baselines which calculate the semantic grid either from the RGB image alone or from LIDAR output alone, showing the interest of this hybrid approach.