A flexible and scalable SLAM system with full 3D motion estimation

For many applications in Urban Search and Rescue (USAR) scenarios robots need to learn a map of unknown environments. We present a system for fast online learning of occupancy grid maps requiring low computational resources. It combines a robust scan matching approach using a LIDAR system with a 3D attitude estimation system based on inertial sensing. By using a fast approximation of map gradients and a multi-resolution grid, reliable localization and mapping capabilities in a variety of challenging environments are realized. Multiple datasets showing the applicability in an embedded hand-held mapping system are provided. We show that the system is sufficiently accurate as to not require explicit loop closing techniques in the considered scenarios. The software is available as an open source package for ROS.

[1]  Wolfram Burgard,et al.  Efficient Sparse Pose Adjustment for 2D mapping , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Alexander Kleiner,et al.  Operator-assistive mapping in harsh environments , 2009, 2009 IEEE International Workshop on Safety, Security & Rescue Robotics (SSRR 2009).

[3]  Frank Dellaert,et al.  iSAM: Incremental Smoothing and Mapping , 2008, IEEE Transactions on Robotics.

[4]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[5]  Edwin Olson,et al.  Real-time correlative scan matching , 2009, 2009 IEEE International Conference on Robotics and Automation.

[6]  Johannes Meyer,et al.  A Flexible Real-Time Control System for Autonomous Vehicles , 2010, ISR/ROBOTIK.

[7]  John Weston,et al.  Strapdown Inertial Navigation Technology , 1997 .

[8]  Bernt Schiele,et al.  A Semantic World Model for Urban Search and Rescue Based on Heterogeneous Sensors , 2010, RoboCup.

[9]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[10]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[11]  Leif Kobbelt,et al.  Iterative multi - view plane fitting , 2006 .

[12]  Nicholas Roy,et al.  Autonomous Flight in Unknown Indoor Environments , 2009 .

[13]  Antoni Burguera,et al.  On the use of likelihood fields to perform sonar scan matching localization , 2009, Auton. Robots.

[14]  Lindsay Kleeman,et al.  Fast Laser Scan Matching using Polar Coordinates , 2007, Int. J. Robotics Res..

[15]  Wolfram Burgard,et al.  Towards a navigation system for autonomous indoor flying , 2009, 2009 IEEE International Conference on Robotics and Automation.

[16]  J. Kuipers.  Quaternions and Rotation Sequences , 1998 .

[17]  Ryan Halterman,et al.  Velodyne HDL-64E lidar for unmanned surface vehicle obstacle detection , 2010, Defense + Commercial Sensing.

[18]  Sven Behnke,et al.  Sancta simplicitas - on the efficiency and achievable results of SLAM using ICP-based incremental registration , 2010, 2010 IEEE International Conference on Robotics and Automation.

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

[20]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.

[21]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[22]  Jeffrey K. Uhlmann,et al.  Using covariance intersection for SLAM , 2007, Robotics Auton. Syst..

[23]  Peter Biber,et al.  The normal distributions transform: a new approach to laser scan matching , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[24]  Frank Dellaert,et al.  Incremental smoothing and mapping , 2008 .

[25]  Jizhong Xiao,et al.  An open-source pose estimation system for micro-air vehicles , 2011, 2011 IEEE International Conference on Robotics and Automation.