Ground height map generation for a humanoid robot in an unstructured environment

This paper proposes a method to measure an accurate ground height map for a humanoid robot by using a laser scanner in an unstructured environment. To measure accurate dimensions of a surface or an obstacle, a 2D laser scanner is installed on the head of a humanoid robot, called MAHRU-1. By moving the head up and down, 3D depth map around a robot is obtained from the 2D range data. After obtaining the 3D depth map around a robot, a plane segmentation method is applied to the 3D data. At first, all the horizontal and vertical planes in the 3D point map are extracted, and the outliers are removed from the plane. After segmenting the plane from the 3D data, the dimensions and the area of the planes are calculated. The accuracy of the extracted surface is evaluated with experimental results, which show the effectiveness of proposed method to extract the surfaces in the unstructured environment.

[1]  Bum-Jae You,et al.  Knowledge-based control of a humanoid robot , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Chan-Soo Park,et al.  Characterization of the Hokuyo UBG-04LX-F01 2D laser rangefinder , 2010, 19th International Symposium in Robot and Human Interactive Communication.

[3]  Masahiro Fujita,et al.  3D Perception and Environment Map Generation for Humanoid Robot Navigation , 2008, Int. J. Robotics Res..

[4]  Chong-Ho Choi,et al.  Trajectory generation and control for a biped robot walking upstairs , 2010 .

[5]  Christian Goerick,et al.  Fast detection of arbitrary planar surfaces from unreliable 3D data , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Cang Ye,et al.  Characterization of a 2D laser scanner for mobile robot obstacle negotiation , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[7]  Masayuki Inaba,et al.  Autonomous 3D walking system for a humanoid robot based on visual step recognition and 3D foot step planner , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[8]  Roland Siegwart,et al.  A comparison of line extraction algorithms using 2D range data for indoor mobile robotics , 2007, Auton. Robots.

[9]  Phillip J. McKerrow,et al.  Introduction to robotics , 1991 .