3D Mapping for high-fidelity unmanned ground vehicle lidar simulation

Abstract

High-fidelity simulation is a key enabling technology for the widespread deployment of large unmanned ground vehicles (UGVs). However, current approaches for lidar simulation leave much to be desired, particularly for scenes with vegetation. We introduce a novel 3D mapping technique that learns high-fidelity models for geo-specific lidar simulation directly from pose tagged lidar data. We introduce a novel stochastic, volumetric model that captures and can reproduce the statistical interactions of lidar with terrain. We show how to automatically learn the model directly from 3D mapping data collected by a UGV in the target environment. We extend our approach using terrain-classification techniques to develop a hybrid surface-volumetric model that combines the efficiency of surface modeling for areas that are well approximated by large surfaces (e.g. roads, bare earth) with our volumetric approach for more complex areas (e.g. bushes, trees) without sacrificing overall fidelity. We quantitatively compare the performance of our approach against more conventional methods on large outdoor datasets from urban and off-road environments. Our results show significant performance gains using our volumetric and hybrid approaches over the state-of-the-art, laying the ground work for truly high-fidelity simulation engines for UGVs.