A mapping method in uneven terrain based on hand-held laser scanning and PDR

This paper proposes a scheme for mapping in uneven outdoor and undermine environment by combining the use of hand-held laser scanning and inertial measurement unit (IMU). In order to deal with the uneven road surface, a plane mapping algorithm on the laser data was provided. Then two kinds of odometry methods were analysed, one is based on laser scanning and IMU fusion by extended Kalman filter (EKF) ,the other one is an improved pedestrian dead reckoning (PDR) algorithm based upon multi-threshold step detection and hybrid heading estimation. Two experiments were performed, one is besides the first floor of the School of Environmental Science and Spatial Informatics (SESSI) building on the campus of China University of Mining and Technology (CUMT) campus, another is under a gypsum mine located in Shandong Province of China. The results confirm that the PDR algorithm was better in uneven environment to produce the odometry which was used as the input of a map building tool named Gmapping. The proposed scheme can reliably achieve the mapping work in both environments.

[1]  Hans P. Moravec.  Sensor Fusion in Certainty Grids for Mobile Robots , 1988, AI Mag..

[2]  Hugh F. Durrant-Whyte,et al.  Map-building and map-based localization in an underground-mine by statistical pattern matching , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[3]  Hugh F. Durrant-Whyte,et al.  Autonomous underground navigation of an LHD using a combined ICP-EKF approach , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[4]  Timothy S. Bailey,et al.  Mobile Robot Localisation and Mapping in Extensive Outdoor Environments , 2002 .

[5]  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).

[6]  Lindsay Kleeman,et al.  Laser scan matching in polar coordinates with application to SLAM , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[8]  Andrea Censi,et al.  An ICP variant using a point-to-line metric , 2008, 2008 IEEE International Conference on Robotics and Automation.

[9]  Mark E. Campbell,et al.  Probabilistic estimation of Multi-Level terrain maps , 2009, 2009 IEEE International Conference on Robotics and Automation.

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

[11]  Jizhong Xiao,et al.  3 D Indoor Mapping for Micro-UAVs Using Hybrid Range Finders and Multi-Volume Occupancy Grids , 2010 .

[12]  Feng Zhao,et al.  A reliable and accurate indoor localization method using phone inertial sensors , 2012, UbiComp.

[13]  Youngnam Han,et al.  Improved heading estimation for smartphone-based indoor positioning systems , 2012, 2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC).

[14]  P. Robertson,et al.  Unscented Kalman filter and Magnetic Angular Rate Update (MARU) for an improved Pedestrian Dead-Reckoning , 2012, Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium.

[15]  Hyung Seok Kim,et al.  Enhanced weighted K-nearest neighbor algorithm for indoor Wi-Fi positioning systems , 2012, 2012 8th International Conference on Computing Technology and Information Management (NCM and ICNIT).

[16]  Naser El-Sheimy,et al.  Map Aided Pedestrian Dead Reckoning Using Buildings Information for Indoor Navigation Applications , 2013 .

[17]  Henri Nurminen,et al.  Particle filter and smoother for indoor localization , 2013, International Conference on Indoor Positioning and Indoor Navigation.

[18]  Fernando Seco Granja,et al.  Improving Inertial Pedestrian Dead-Reckoning by Detecting Unmodified Switched-on Lamps in Buildings , 2014, Sensors.

[19]  Michael Bosse,et al.  Efficient Large‐scale Three‐dimensional Mobile Mapping for Underground Mines , 2014, J. Field Robotics.

[20]  Jian Wang,et al.  Integrated WiFi/PDR/Smartphone Using an Adaptive System Noise Extended Kalman Filter Algorithm for Indoor Localization , 2016, ISPRS Int. J. Geo Inf..