Projective ground segmentation and complete object recovery for perceptual terrain reconstruction

Abstract

Terrain reconstruction and photorealistic visualization are required for the remote operation of mobile robots. We can generate textured terrain meshes by using 2D and 3D datasets obtained from multiple sensors. To detect the traversable regions of a terrain, we apply the Gibbs-Markov random field (MRF) model with a flood-fill algorithm to segment the ground and objects in the reconstructed terrain mesh and 2D images. We propose a height estimation method that recovers missing parts by finding object boundaries in 2D images and estimating the 3D coordinates of the boundaries. Our proposed methods were tested in an outdoor environment. The results show that ground data can be segmented effectively and that the unsensed parts of objects can be accurately recovered. ©2014 International Information Institute.