Evidential occupancy grid maps (OGMs) are a popular representation of the environment of automated vehicles. Inverse sensor models (ISMs) are used to compute OGMs from sensor data such as lidar point clouds. Geometric ISMs show a limited performance when estimating states in unobserved but inferable areas and have difficulties dealing with ambiguous input. Deep learning-based ISMs face the challenge of limited training data and they often cannot handle uncertainty quantification yet. We propose a deep learning-based framework for learning an OGM algorithm which is both capable of quantifying first- and second-order uncertainty and which does not rely on manually labeled data. Results on synthetic and on real-world data show superiority over other approaches. Source code and datasets are available at https://github.com/ika-rwth-aachen/EviLOG
@article{arxiv.2102.12718,
title = {A Simulation-based End-to-End Learning Framework for Evidential Occupancy Grid Mapping},
author = {Raphael van Kempen and Bastian Lampe and Timo Woopen and Lutz Eckstein},
journal= {arXiv preprint arXiv:2102.12718},
year = {2021}
}
Comments
Accepted to be published as part of the 2021 IEEE Intelligent Vehicles Symposium (IV), Nagoya, Japan, July 11-15, 2021