Appearance-based Localization across Seasons in a Metric Map

In this paper we address the problem of appearance-based long-term outdoor localization across seasons. This is a difficult task due to the changing appearance of visual landmarks across seasons and time of day. Our approach operates based on the premise that combining visual landmarks observed at different times of the year into a single metric map will yield better localization results than a map created from a single sequence alone. We integrate stereo imagery collected at two different times of the year into a unified 3D map, and use this as the basis for localization. A landmark visibility prediction framework is utilized to efficiently retrieve a small subset of landmarks and their feature descriptors from a database of millions of landmarks. The proposed approach is experimentally validated on a challenging sequence collected a year earlier.

[1]  Richard Szeliski,et al.  Building Rome in a day , 2009, ICCV.

[2]  Guang-Zhong Yang,et al.  Dynamic scene models for incremental, long-term, appearance-based localisation , 2013, 2013 IEEE International Conference on Robotics and Automation.

[3]  Achim J. Lilienthal,et al.  SIFT, SURF & seasons: Appearance-based long-term localization in outdoor environments , 2010, Robotics Auton. Syst..

[4]  Jan-Michael Frahm,et al.  A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus , 2008, ECCV.

[5]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[6]  Henning Lategahn,et al.  City GPS using stereo vision , 2012, 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES 2012).

[7]  Gordon Wyeth,et al.  CAT-SLAM: probabilistic localisation and mapping using a continuous appearance-based trajectory , 2012, Int. J. Robotics Res..

[8]  Guang-Zhong Yang,et al.  Feature Co-occurrence Maps: Appearance-based localisation throughout the day , 2013, 2013 IEEE International Conference on Robotics and Automation.

[9]  Steven M. Seitz,et al.  Photo tourism: exploring photo collections in 3D , 2006, ACM Trans. Graph..

[10]  Jiri Matas,et al.  Matching with PROSAC - progressive sample consensus , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[12]  Daniel P. Huttenlocher,et al.  Location Recognition Using Prioritized Feature Matching , 2010, ECCV.

[13]  Winston Churchill,et al.  Experience-based navigation for long-term localisation , 2013, Int. J. Robotics Res..

[14]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[15]  Frank Dellaert,et al.  Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing , 2006, Int. J. Robotics Res..

[16]  Frank Dellaert,et al.  Visibility learning in large-scale urban environment , 2011, 2011 IEEE International Conference on Robotics and Automation.

[17]  Torsten Sattler,et al.  Fast image-based localization using direct 2D-to-3D matching , 2011, 2011 International Conference on Computer Vision.

[18]  Torsten Sattler,et al.  Improving Image-Based Localization by Active Correspondence Search , 2012, ECCV.

[19]  Julius Ziegler,et al.  Urban localization with camera and inertial measurement unit , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[20]  Gordon Wyeth,et al.  SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights , 2012, 2012 IEEE International Conference on Robotics and Automation.

[21]  Jan-Michael Frahm,et al.  Improved Geometric Verification for Large Scale Landmark Image Collections , 2012, BMVC.

[22]  Pascal Fua,et al.  Worldwide Pose Estimation Using 3D Point Clouds , 2012, ECCV.

[23]  Paul Newman,et al.  FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance , 2008, Int. J. Robotics Res..