Dense accurate urban mapping from spherical RGB-D images

This paper presents a methodology to combine information from a sequence of RGB-D spherical views acquired by a home-made multi-stereo device in order to improve the computed depth images both in terms of accuracy and completeness. This methodology is embedded in a larger visual mapping framework aiming to produce accurate and dense topometric urban maps. Our method is based on two main filtering stages. Firstly, we perform a segmentation process considering both geometric and photometric image constraints, followed by a regularization step (spatial-integration). We then proceed to a fusion stage where the geometric information is further refined by considering the depth images of nearby frames (temporal integration). This methodology can be applied to other projective models, such as perspective stereo images. Our approach is evaluated within the frameworks of image registration, localization and mapping, demonstrating higher accuracy and larger convergence domains over different datasets.