Room-structure estimation in Manhattan-like environments from dense 2½D range data using minumum entropy and histograms

In this paper we propose a novel approach for the robust estimation of room structure using Manhattan world assumption i.e. the frequently observed dominance of three mutually orthogonal vanishing directions in man-made environments. First, separate histograms are generated for every major axis, i.e. X, Y and Z, on stereo data with an arbitrary roll, pitch and yaw rotation. These histograms are maintained in the fashion of quadtrees. Using the traditional Markov particle filters and minimal entropy as metric on the histograms, we are able to estimate the camera orientation with respect to orthogonal structure. Once the orientation is estimated we extract hypothesis of the room structure by exploiting 2D histograms, i.e. X/Y, Z/Y, Z/X, using mean shift clustering techniques. Finally, the hypotheses are evaluated with the real data and false hypothesis are pruned. We also show the robustness of our approach with respect to noise in real world data.