To create global map we take obstacles as natural landmarks, it is however necessary to find the correspondences between landmarks by use of a Bhatacharayya distance. Our experiments demonstrate the functionality of estimating the exact pose of the robot and the consistence of the global map of the environment. | Journal of Automation and Control Engineering, Vol. 1, No. 2, June 2013 6D-Slam with Navigable Space Discovering Boutine Rachid Department of computer science 20 august 1955 university, Skikda, Algeria Boutine_rachid@ Benmohammed Mohamed LIRE Laboratory Mentouri university, Constantine, Algeria Ben_moh123@ Abstract—For all mobiles robots that navigate in unknown outdoor environments two basic tasks are required: discovering navigable space, and obstacles detection. In this work, we have proposed a new variant of the 6d-slam framework, wherein planar patches were used for representing navigable area, and 3d mesh for representing obstacles. A decomposition of the environment in smaller voxels is made by an octree structure. Adjacent voxels with no empty intersection of their best fitting plans should be piece together in the same navigable space; otherwise adjacent non planar voxels would form the seed of potential obstacles. To create global map we take obstacles as natural landmarks, it is however necessary to find the correspondences between landmarks by use of a Bhatacharayya distance. Our experiments demonstrate the functionality of estimating the exact pose of the robot and the consistence of the global map of the environment. Index Terms—6d-Slam, operation, Octree I. Bhatacharayya distance, the fourth section, we present a solution for navigable space growth problem, and in the fifth section, we propose a novel association method, in the sixth section, we present the proposed 6d-slam variant, finally in the last section, we give the experimental results. II. In the last decade, a great deal of latest works are oriented towered 6D-Slam, like the work of Paloma de la Puente and all [1], [2], where they present a segmentation and fitting algorithm of 3d points clouds, based on computer vision techniques, wherein a least-squares fitting, and a maximum Incremental probability algorithm formulated upon the Extended Kalman Filter, were .
