Robust place recognition for 3D range data based on point features

The problem of place recognition appears in different mobile robot navigation problems including localization, SLAM, or change detection in dynamic environments. Whereas this problem has been studied intensively in the context of robot vision, relatively few approaches are available for three-dimensional range data. In this paper, we present a novel and robust method for place recognition based on range images. Our algorithm matches a given 3D scan against a database using point features and scores potential transformations by comparing significant points in the scans. A further advantage of our approach is that the features allow for a computation of the relative transformations between scans which is relevant for registration processes. Our approach has been implemented and tested on different 3D data sets obtained outdoors. In several experiments we demonstrate the advantages of our approach also in comparison to existing techniques.

[1]  Andreas Nüchter,et al.  Automatic Appearance-Based Loop Detection from 3 D Laser Data Using the Normal Distributions Transform , 2009 .

[2]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using unit quaternions , 1987 .

[3]  Paul Newman,et al.  Highly scalable appearance-only SLAM - FAB-MAP 2.0 , 2009, Robotics: Science and Systems.

[4]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[5]  Michael Bosse,et al.  Map Matching and Data Association for Large-Scale Two-dimensional Laser Scan-based SLAM , 2008, Int. J. Robotics Res..

[6]  Karl Granström,et al.  Learning to detect loop closure from range data , 2009, 2009 IEEE International Conference on Robotics and Automation.

[7]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[8]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[9]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[10]  Wolfram Burgard,et al.  Nonlinear Constraint Network Optimization for Efficient Map Learning , 2009, IEEE Transactions on Intelligent Transportation Systems.

[11]  M. Hebert,et al.  Automatic three-dimensional modeling from reality , 2002 .

[12]  Wolfram Burgard,et al.  Robust on-line model-based object detection from range images , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..