DPPTAM: Dense piecewise planar tracking and mapping from a monocular sequence

This paper proposes a direct monocular SLAM algorithm that estimates a dense reconstruction of a scene in real-time on a CPU. Highly textured image areas are mapped using standard direct mapping techniques 1, that minimize the photometric error across different views. We make the assumption that homogeneous-color regions belong to approximately planar areas. Our contribution is a new algorithm for the estimation of such planar areas, based on the information of a superpixel segmentation and the semidense map from highly textured areas. We compare our approach against several alternatives using the public TUM dataset 2 and additional live experiments with a hand-held camera. We demonstrate that our proposal for piecewise planar monocular SLAM is faster, more accurate and more robust than the piecewise planar baseline 3. In addition, our experimental results show how the depth regularization of monocular maps can damage its accuracy, being the piecewise planar assumption a reasonable option in indoor scenarios.