Incremental Reconstruction of Urban Environments by Edge-Points Delaunay Triangulation
This work addresses incremental mapping for resource-limited systems like autonomous vehicles, but it is incremental as it builds on existing triangulation methods with specific heuristics.
The paper tackled urban reconstruction from monocular video for online mapping tasks by proposing an Edge-Points Delaunay Triangulation method with an Inverse Cone Heuristic to avoid artifacts, achieving evaluation on the KITTI dataset against Velodyne measurements.
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.