Data Fusion by Uncertain Projective Geometry in 6DoF Visual SLAM

In this paper we face the issue of fusing 3D data from different sensors in a seamless way, using the unifying framework of uncertain projective geometry. Within this framework it is possible to describe, combine, and estimate various types of geometric elements (2D and 3D points, 2D and 3D lines, and 3D planes) taking their uncertainty into account. Because of the size of the data involved in this process, the integration process and thus the SLAM algorithm turns out to be very slow. For this reason, in this work, we propose the use of an R*-Tree data structure to speed up the whole process, managing in an efficent way both the estimated map and the 3D points clouds coming out from the stereo camera. The experimental section shows that the use of uncertain projective geometry and the R*-Tree data structure improves the mapping and the pose estimation.