Using superpixels in monocular SLAM

Monocular SLAM and Structure from Motion have been traditionally based on finding point correspondences in highly-textured image areas. Large textureless regions, usually found in indoor and urban environments, are difficult to reconstruct by these systems. In this paper we augment for the first time the traditional point-based monocular SLAM maps with superpixels. Superpixels are middle-level features consisting of image regions of homogeneous texture. We propose a novel scheme for superpixel matching, 3D initialization and optimization that overcomes the difficulties of salient point-based approaches in these areas of homogeneous texture. Our experimental results show the validity of our approach. First, we compare our proposal with a state-of-the-art multiview stereo system; being able to reconstruct the textureless regions that the latest cannot. Secondly, we present experimental results of our algorithm integrated with the point-based PTAM [1]; estimating, now in real-time, the superpixel textureless areas. Finally, we show the accuracy of the presented algorithm with a quantitative analysis of the estimation error.