Incremental Reconstruction of Urban Environments by Edge-Points Delaunay Triangulation

TL;DR

This paper presents an incremental method for urban environment reconstruction using edge-point Delaunay triangulation from monocular video, enabling online mapping suitable for obstacle avoidance and traversability analysis.

Contribution

It introduces a novel approach utilizing edge-points for Delaunay triangulation and an Inverse Cone Heuristic to improve urban environment reconstruction from monocular video.

Findings

Effective reconstruction of urban scenes demonstrated on KITTI dataset

Reconstruction accuracy comparable to Velodyne measurements

Manifold surface reconstruction enables further graphics processing

Abstract

Urban reconstruction from a video captured by a surveying vehicle constitutes a core module of automated mapping. When computational power represents a limited resource and, a detailed map is not the primary goal, the reconstruction can be performed incrementally, from a monocular video, carving a 3D Delaunay triangulation of sparse points; this allows online incremental mapping for tasks such as traversability analysis or obstacle avoidance. To exploit the sharp edges of urban landscape, we propose to use a Delaunay triangulation of Edge-Points, which are the 3D points corresponding to image edges. These points constrain the edges of the 3D Delaunay triangulation to real-world edges. Besides the use of the Edge-Points, a second contribution of this paper is the Inverse Cone Heuristic that preemptively avoids the creation of artifacts in the reconstructed manifold surface. We force the reconstruction of a manifold surface since it makes it possible to apply computer graphics or photometric refinement algorithms to the output mesh. We evaluated our approach on four real sequences of the public available KITTI dataset by comparing the incremental reconstruction against Velodyne measurements.