Semantic grid estimation with a hybrid Bayesian and deep neural network approach

Erkent, Ozgur, Wolf, Christian, Laugier, Christian, Gonzalez, David Sierra and Romero Cano, Victor ORCID: https://orcid.org/0000-0003-2910-5116 2019. Semantic grid estimation with a hybrid Bayesian and deep neural network approach. Presented at: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 1-5 October 2018. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 10.1109/IROS.2018.8593434

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
Date of First Compliant Deposit:	14 November 2024
Date of Acceptance:	20 April 2018
Last Modified:	20 Dec 2024 02:30
URI:	https://orca.cardiff.ac.uk/id/eprint/174014

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