FLIRT - Interest regions for 2D range data

Local image features are used for a wide range of applications in computer vision and range imaging. While there is a great variety of detector-descriptor combinations for image data and 3D point clouds, there is no general method readily available for 2D range data. For this reason, the paper first proposes a set of benchmark experiments on detector repeatability and descriptor matching performance using known indoor and outdoor data sets for robot navigation. Secondly, the paper introduces FLIRT that stands for Fast Laser Interest Region Transform, a multi-scale interest region operator for 2D range data. FLIRT combines the best detector with the best descriptor, experimentally found in a comprehensive analysis of alternative detector and descriptor approaches. The analysis yields repeatability and matching performance results similar to the values found for features in the computer vision literature, encouraging a wide range of applications of FLIRT on 2D range data. We finally show how FLIRT can be used in conjunction with RANSAC to address the loop closing/global localization problem in SLAM in indoor as well as outdoor environments. The results demonstrate that FLIRT features have a great potential for robot navigation in terms of precision-recall performance, efficiency and generality.

[1]  Gabriel Taubin,et al.  A signal processing approach to fair surface design , 1995, SIGGRAPH.

[2]  Roland Siegwart,et al.  Feature extraction and scene interpretation for map-based navigation and map building , 1998, Other Conferences.

[3]  Gérard G. Medioni,et al.  Robust estimation of curvature information from noisy 3D data for shape description , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[4]  Wolfram Burgard,et al.  MINERVA: a second-generation museum tour-guide robot , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

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

[6]  Roland Siegwart,et al.  A Navigation Framework for Multiple Mobile Robots and its Application , 2003 .

[7]  Cordelia Schmid,et al.  Affine-invariant local descriptors and neighborhood statistics for texture recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[8]  Markus H. Gross,et al.  Multi‐scale Feature Extraction on Point‐Sampled Surfaces , 2003, Comput. Graph. Forum.

[9]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[10]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[12]  Wolfram Burgard,et al.  A Tree Parameterization for Efficiently Computing Maximum Likelihood Maps using Gradient Descent , 2007, Robotics: Science and Systems.

[13]  Ko Nishino,et al.  Scale-Dependent 3D Geometric Features , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[14]  Martial Hebert,et al.  Multi-scale interest regions from unorganized point clouds , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

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

[16]  Michael Bosse,et al.  Keypoint design and evaluation for place recognition in 2D lidar maps , 2009, Robotics Auton. Syst..

[17]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.