3D feature based mapping towards mobile robots' enhanced performance in rescue missions

This paper presents a feature based 3D mapping approach with regard to obtaining compact models of semi-structured environments such as partially destroyed buildings where mobile robots are to carry out rescue activities. To gather the 3D data, we use a laser scanner, employing a nodding data acquisition system mounted on both real and simulated robots. Our segmentation algorithm comes up from the integration of computer vision techniques, allowing for a fast separation of points corresponding to different, not necessarily planar, surfaces. The subsequent extraction of geometrical features out of each region's points is done by means of least-squares fitting. A Maximum Incremental Probability algorithm formulated upon the Extended Kalman Filter provides 6D localization and produces a map of planar patches with a convex-hull based representation. Scenarios from the Unified System for Automation and Robot Simulation (USARSim), including world models from past RoboCup Rescue editions' arenas, have been utilized to conduct some of our experiments.

[1]  Johannes Pellenz,et al.  Mapping and Map Scoring at the RoboCupRescue Competition , 2008 .

[2]  Michel Devy,et al.  SLAM and data fusion from visual landmarks and 3D planes , 2008 .

[3]  Paul Newman,et al.  Using laser range data for 3D SLAM in outdoor environments , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[4]  T. Rabbani,et al.  SEGMENTATION OF POINT CLOUDS USING SMOOTHNESS CONSTRAINT , 2006 .

[5]  Daniele Nardi,et al.  Multi‐objective exploration and search for autonomous rescue robots , 2007, J. Field Robotics.

[6]  Jean Scholtz,et al.  Evaluation of human-robot interaction awareness in search and rescue , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[7]  Fernando Matía,et al.  Extraction of Geometrical Features in 3D Environments for Service Robotic Applications , 2008, HAIS.

[8]  A. Bykat,et al.  Convex Hull of a Finite Set of Points in Two Dimensions , 1978, Inf. Process. Lett..

[9]  Massimo Mecella,et al.  The Advantage of Mobility: Mobile Tele-operation for Mobile Robots , 2009 .

[10]  Michael A. Goodrich,et al.  Comparing the usefulness of video and map information in navigation tasks , 2006, HRI '06.

[11]  Bernardo Wagner,et al.  Using 3D laser range data for SLAM in outdoor environments , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[12]  Wolfram Burgard,et al.  A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[13]  Alessandro Farinelli,et al.  Multi-objective exploration and search for autonomous rescue robots: Research Articles , 2007 .

[14]  George Vosselman,et al.  Segmentation of point clouds using smoothness constraints , 2006 .

[15]  Wolfram Burgard,et al.  A system for volumetric robotic mapping of abandoned mines , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[16]  Ramón Galán,et al.  Implementing Map Based Navigation in Guido, the Robotic SmartWalker , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[17]  Roland Siegwart,et al.  EKF-based 3D SLAM for structured environment reconstruction , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Jaime Valls Miró,et al.  BS-SLAM: Shaping the World , 2007, Robotics: Science and Systems.

[19]  Herman Bruyninckx,et al.  Kalman Filters : A Tutorial , 1999 .

[20]  Wolfram Burgard,et al.  Using Hierarchical EM to Extract Planes from 3D Range Scans , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[21]  Holly A. Yanco,et al.  LASSOing HRI: Analyzing situation awareness in map-centric and video-centric interfaces , 2007, 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[22]  Diego Rodríguez-Losada González.  Slam geométrico en tiempo real para robots móviles en interiores basado en ekf , 2004 .

[23]  Roland Siegwart,et al.  3D SLAM using planar segments , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[24]  Joachim Hertzberg,et al.  3D Mapping with Semantic Knowledge , 2005, RoboCup.

[25]  Wolfram Burgard,et al.  Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters , 2007, IEEE Transactions on Robotics.

[26]  Joachim Hertzberg,et al.  6D SLAM with Cached kd-tree Search , 2006, Robot Navigation.

[27]  Andreas Nüchter,et al.  6D SLAM with Kurt 3D , 2007 .

[28]  J. M. M. Montiel,et al.  The SPmap: a probabilistic framework for simultaneous localization and map building , 1999, IEEE Trans. Robotics Autom..

[29]  Dan R. Olsen,et al.  Metrics for Evaluating Human-Robot Interactions , 2003 .

[30]  Joachim Hertzberg,et al.  6D SLAM—3D mapping outdoor environments , 2007, J. Field Robotics.

[31]  Joachim Hertzberg,et al.  Evolving interface design for robot search tasks: Research Articles , 2007 .

[32]  Rudolph Triebel,et al.  Three-dimensional perception for mobile robots , 2008 .