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Humanoids have recently become a popular research platform in the robotics community. Such robots offer various fields for new applications. However, they have several drawbacks compared to wheeled vehicles such as stability problems, limited payload capabilities, violation of the flat world assumption, and they typically provide only very rough odometry information, if at all. In this paper, we investigate the problem of learning accurate grid maps with humanoid robots. We present techniquesdoi:10.1109/robot.2008.4543697 dblp:conf/icra/StachnissBGBB08 fatcat:2wkvjttpdra3jii3noc524bpou