A humanoid robot that can go up and down stairs, crawl underneath obstacles or simply walk around requires reliable perceptual capabilities for obtaining accurate and useful information about its surroundings. In this work we present a system for generating threedimensional (3D) environment maps from data taken by stereo vision. At the core is a method for precise segmentation of range data into planar segments based on the algorithm of scan-line grouping extended to cope with the noise dynamics of stereo vision. In off-line experiments we demonstrate that our extensions achieve a more precise segmentation. When compared to a previously developed patchlet method, we obtain a richer segmentation with a higher accuracy while also requiring far less computations. From the obtained segmentation we then build a 3D environment map using occupancy grid and floor height maps. The resulting representation classifies areas into one of six different types while also providing object height information. We apply our perception method for the navigation of the humanoid robot QRIO and present experiments of the robot stepping through narrow space, walking up and down stairs and crawling underneath a table.KEY WORDS-humanoid robot navigation, 3D environment perception, range image segmentation, stereo vision
Abstract-With the development of biped robots, systems became able to navigate in a 3 dimensional world, walking up and down stairs, or climbing over small obstacles. We present a method for obtaining a labeled 2.5D grid map of the robot's surroundings. Each cell is marked either as floor or obstacle and contains a value telling the height of the floor or obstacle. Such height maps are useful for path planning and collision avoidance. The method uses a novel combination of a 3D occupancy grid for robust sensor data interpretation and a 2.5D height map for fine resolution floor values. We evaluate our approach using stereo vision on the humanoid robot QRIO and show the advantages over previous methods. Experimental results from navigation runs on an obstacle course demonstrate the ability of the method to generate detailed maps for autonomous navigation.Index Terms-3D perception and navigation, obstacle avoidance, humanoid robot.
For the fully autonomous navigation in a 3 dimensional world, B humanoid robot must be capable of stepping up and down staireases and other obstacle where a dlident large flat surface can support the robot's feet This paper presene metbods for the recognition of stairs and a conhol architecture that enable the humanoid robot QRIO to safely climb up and down In its environment The approach is based on data captured by a s t e m vision system and segmented into planar surfam. From the segmented planes, stairs that can he climbed by the robot are extracted nnd fed to a control system which decides the action to be taken next Experimental rwvlts on a stnircase as well as climbing up and down a sill are presented.
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