A Simulation-based End-to-End Learning Framework for Evidential Occupancy Grid Mapping

IEEE Intelligent Vehicles Symposium, pp. 934–939

Abstract

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.

Authors 4

1. Raphael van Kempen Aachen Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Automotive Engineering (ika), RWTH Aachen University, Aachen, Germany

   Institute for Automotive Engineering (ika), RWTH Aachen University,Aachen,Germany,52074

2. Bastian Lampe Aachen Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Automotive Engineering (ika), RWTH Aachen University, Aachen, Germany

   RWTH Aachen University

3. Timo Woopen Aachen Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Automotive Engineering (ika), RWTH Aachen University, Aachen, Germany

   Institute for Automotive Engineering (ika), RWTH Aachen University,Aachen,Germany,52074

4. Lutz Eckstein Aachen Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Automotive Engineering (ika), RWTH Aachen University, Aachen, Germany

   Institute for Automotive Engineering (ika), RWTH Aachen University,Aachen,Germany,52074