Solid Model Reconstruction of Large-Scale Outdoor Scenes from 3D Lidar Data

Abstract

Globally consistent 3D maps are commonly used for robot mission navigation, and teleoperation in unstructured and uncontrolled environments. These maps are typically represented as 3D point clouds; however other representations, such as surface or solid models, are often required for humans to perform scientific analyses, infrastructure planning, or for general visualization purposes. Robust large-scale solid model reconstruction from point clouds of outdoor scenes can be challenging due to the presence of dynamic objects, the ambiguitiy between non-returns and sky-points, and scalability requirements. Volume-based methods are able to remove spurious points arising from moving objects in the scene by considering the entire ray of each measurement, rather than simply the end point. Scalability can be addressed by decomposing the overall space into multiple tiles, from which the resulting surfaces can later be merged. We propose an approach that applies a weighted signed distance function along each measurement ray, where the weight indicates the confidence of the calculated distance. Due to the unenclosed nature of outdoor environments, we introduce a technique to automatically generate a thickened structure in order to model surfaces seen from only one side. The final solid models are thus suitable to be physically printed by a rapid prototyping machine.The approach is evaluated on 3D laser point cloud data collected from a mobile lidar in unstructured and uncontrolled environments, including outdoors and inside caves. The accuracy of the solid model reconstruction is compared to a previously developed binary voxel carving method. The results show that the weighted signed distance approach produces a more accurate reconstruction of the surface, and since higher accuracy models can be produced at lower resolutions, this additionally results in significant improvements in processing time.