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This work introduces an Occupancy-Elevation Grid (OEG) mapping technique, which is a discrete mapping approach where each cell represents the occupancy probability, the height of the terrain and its variance. This representation allows a mobile robot to know with an accurate degree of certainty whether a place in the environment is occupied by an obstacle and the height of such obstacle. Thus, based on its hardware characteristics, it can make calculations to decide if it is possible to traverse that specific place. In general, the map representation introduced can be used in conjunction with any kind of distance sensor. In this work, we use laser range data and stereo system data with a probabilistic treatment. The resulting maps allow the execution of tasks as decision making for autonomous navigation, exploration, localization and path planning, considering the existence and the height of the obstacles. Experiments carried out with real data demonstrate that the proposed approach yields useful maps for autonomous navigation.<\/jats:p>","DOI":"10.1017\/s0263574715000235","type":"journal-article","created":{"date-parts":[[2015,4,15]],"date-time":"2015-04-15T05:07:09Z","timestamp":1429074429000},"page":"2592-2609","source":"Crossref","is-referenced-by-count":32,"title":["Occupancy-elevation grid: an alternative approach for robotic mapping and navigation"],"prefix":"10.1017","volume":"34","author":[{"given":"Anderson","family":"Souza","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luiz M. G.","family":"Gon\u00e7alves","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2015,4,15]]},"reference":[{"key":"S0263574715000235_ref21","doi-asserted-by":"publisher","DOI":"10.1002\/rob.20165"},{"key":"S0263574715000235_ref18","doi-asserted-by":"crossref","unstructured":"I. Dryanovski , W. Morris and J. Xiao , \u201cMulti-Volume Occupancy Grids: An Efficient Probabilistic 3d Mapping Model for Micro Aerial Vehicles,\u201d Proceedings of IEEE\/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan (2010) pp. 1553\u20131559.","DOI":"10.1109\/IROS.2010.5652494"},{"key":"#cr-split#-S0263574715000235_ref13.2","unstructured":"3. (IV), Alcal?? de Henares, Spain (2012) pp. 802-807."},{"key":"S0263574715000235_ref3","unstructured":"E. Einhorn , C. Schr\u00f6ter and H.-M. Gross , \u201cFinding the Adequate Resolution for Grid Mapping - Cell Size Locally Adapting on-the-Fly,\u201d Proceeding of IEEE International Conference on Robotic and Automation, Shanghai, China (2011) pp. 1843\u20131848."},{"key":"S0263574715000235_ref25","unstructured":"K. M. Wurm , A. Hornung , M. Bennewitz , C. Stachniss and W. Burgard , \u201cOctomap: A Probabilistic, Flexible, and Compact 3d Map Representation for Robotic Systems,\u201d ICRA, Workshop on 3D Perception and Modeling, Anchorage, Alaska, USA (2010)."},{"key":"S0263574715000235_ref12","doi-asserted-by":"crossref","unstructured":"F. Andert , \u201cDrawing Stereo Disparity Images into Occupancy Grids: Measurement Model and Fast Implementation,\u201d Proceedings of IEEE International Conference on Intelligent Robots and Systems, St. Louis, MO, USA (2009) pp. 5191\u20135197.","DOI":"10.1109\/IROS.2009.5354638"},{"key":"S0263574715000235_ref11","unstructured":"H. Chen and Z. Xu , \u201c3d Map Building based on Stereo Vision,\u201d Proceedings of the 2006 IEEE International Conference on Networking, Sensing and Control - ICNSC, Ft. Lauderdale, FL, USA (2006) pp. 969\u2013973."},{"key":"S0263574715000235_ref20","unstructured":"C. Rivadeneyra , I. Miller and M. Campbell , \u201cProbabilistic Estimation of Multi-Level Terrain Maps,\u201d Proceedings of IEEE International Conference on Robotics and Automation, Kobe, Japan (2009) pp. 1643\u20131648."},{"key":"S0263574715000235_ref6","volume-title":"Probabilistic Robotics","author":"Thrun","year":"2005"},{"key":"S0263574715000235_ref2","volume-title":"Exploring Artificial Intelligence in the New Millenium","author":"Thrun","year":"2009"},{"key":"S0263574715000235_ref7","unstructured":"Y. Liu , R. Emery , D. Chakrabarti , W. Burgard and S. Thrun , \u201cUsing EM to Learn 3D Models with Mobile Robots,\u201d Proceedings of the International Conference on Machine Learning (ICML), Bellevue, Washington, USA (2001)."},{"key":"S0263574715000235_ref4","doi-asserted-by":"publisher","DOI":"10.1177\/0278364906075165"},{"key":"S0263574715000235_ref9","unstructured":"H. P. Moravec , \u201cRobot spacial perception by stereoscopic vision on 3d evidence grid,\u201d CMU Robotics Institute, Pittsburg, Pensylvania, Technical Report CMU-RI-TR-96-34, 1996."},{"key":"S0263574715000235_ref8","first-page":"61","article-title":"Sensor fusion in certainty grids for mobile robots","volume":"9","author":"Moravec","year":"1988","journal-title":"Computer"},{"key":"S0263574715000235_ref23","unstructured":"M. Yguel , O. Aycard and C. 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