Journals Proceedings

Abstract

Indoor environment usually has complicated structures and contains planes which can be used as extra features for registration. This paper proposes a method to use planar features extracted from depth camera for indoor localization. Two consecutive depth images are converted to point clouds and segmented to planes. Two sets of planes are then matched together to estimate the rotation matrix which is used as initial guess of iterative closest point (ICP) of point-to-plane registration. Our experimental result shows that ICP of point-toplane algorithm with the extra step of estimating the rotation guess matrix performs faster than conventional ICP and overcomes the drawback of ICP point-to-point algorithm in indoor environment.