A Comparison of G2o Graph SLAM and EKF Pose Based SLAM with Bathymetry Grids

Abstract This paper address the Simultaneous Localization and Mapping (SLAM) problem of an AUV using bathymetric maps. The algorithm compounds swath profiles of the seafloor with DVL navigation(dead-reckoning) to build surface patches (3D point clouds). An initial guess of the location of these point clouds is known a priori by means of the dead-reckoning solution. Whenever there is a significant overlap of two or more point clouds, the corresponding surface patches are registered among themselves using a probabilistic ICP algorithm. The outcome of the registration procedure is a set of constrains defining the relative position of the overlapping surface patches. Next, these constrains are used to optimize a pose graph using the G2o optimizer. The results are compared against our prior EKF-pose-based SLAM solution. Our results suggest that a better performance is achieved using EKF global optimization with respect to the G2o graph-SLAM solution.

[1]  Pere Ridao,et al.  Underwater SLAM in man-made structured environments , 2008 .

[2]  Hanumant Singh,et al.  Consistency based error evaluation for deep sea bathymetric mapping with robotic vehicles , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[3]  Joaquim Salvi,et al.  Fourier-based registrations for two-dimensional forward-looking sonar image mosaicing , 2012, IROS.

[4]  Pere Ridao,et al.  A survey on Terrain Based Navigation for AUVs , 2010, OCEANS 2010 MTS/IEEE SEATTLE.

[5]  R. Stuckey.  Navigational Error Reduction of Underwater Vehicles with Selective Bathymetric SLAM , 2012 .

[6]  Pere Ridao,et al.  Probabilistic surface matching for bathymetry based SLAM , 2013, 2013 IEEE International Conference on Robotics and Automation.

[7]  Stefan B. Williams,et al.  An efficient approach to bathymetric SLAM , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  John J. Leonard,et al.  Pure range-only sub-sea SLAM , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[9]  Stefan B. Williams,et al.  Simultaneous Localisation and Mapping and Dense Stereoscopic Seafloor Reconstruction Using an AUV , 2008, ISER.

[10]  Stefan B. Williams,et al.  A featureless approach to efficient bathymetric SLAM using distributed particle mapping , 2011, J. Field Robotics.

[11]  Hanumant Singh,et al.  A Self‐Consistent Bathymetric Mapping Algorithm , 2007, J. Field Robotics.

[12]  Wolfram Burgard,et al.  G2o: A general framework for graph optimization , 2011, 2011 IEEE International Conference on Robotics and Automation.