Appearance-based concurrent map building and localization using a multi-hypotheses tracker

The main drawback of appearance-based robot localization with respect to landmark-based one is that it requires a map including images taken at known positions in the area where the robot is expected to move. In this paper, we describe a concurrent map-building and localization (CML) system developed within the appearance-base robot localization paradigm. This allows us to combine the good features of appearance-base localization, such as simple sensor processing or robustness, without having to deal with its inconveniences. In our CML system, both the robot's position and the map are represented using Gaussian mixtures. Using this kind of representation, we can deal with the global localization problem while being efficient both in memory and in execution time.

[1]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[2]  Wolfram Burgard,et al.  Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids , 1996, AAAI/IAAI, Vol. 2.

[3]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[4]  Ferdinand van der Heijden,et al.  Recursive unsupervised learning of finite mixture models , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Ben J. A. Kröse,et al.  Omnidirectional Vision for Appearance-Based Robot Localization , 2000, Sensor Based Intelligent Robots.

[6]  Ben Kröse,et al.  A particle filter to estimate non-Markovian states , 2004 .

[7]  Hiroshi Murase,et al.  Visual learning and recognition of 3-d objects from appearance , 2005, International Journal of Computer Vision.

[8]  Sebastian Thrun,et al.  FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges , 2003, IJCAI 2003.

[9]  Gregory Dudek,et al.  Comparing image-based localization methods , 2003, IJCAI.

[10]  Jeffrey K. Uhlmann,et al.  Nondivergent simultaneous map building and localization using covariance intersection , 1997, Defense, Security, and Sensing.

[11]  Viii Supervisor.  Sonar-Based Real-World Mapping and Navigation , 2001 .

[12]  Evangelos E. Milios,et al.  Globally Consistent Range Scan Alignment for Environment Mapping , 1997, Auton. Robots.

[13]  Alex M. Andrew,et al.  Artificial Intelligence and Mobile Robots , 1999 .

[14]  Hugh F. Durrant-Whyte,et al.  Mobile robot localization by tracking geometric beacons , 1991, IEEE Trans. Robotics Autom..

[15]  Kurt Konolige,et al.  Incremental mapping of large cyclic environments , 1999, Proceedings 1999 IEEE International Symposium on Computational Intelligence in Robotics and Automation. CIRA'99 (Cat. No.99EX375).

[16]  Ingemar J. Cox,et al.  Modeling a Dynamic Environment Using a Bayesian Multiple Hypothesis Approach , 1994, Artif. Intell..

[17]  P. Yip,et al.  Discrete Cosine Transform: Algorithms, Advantages, Applications , 1990 .

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

[19]  Hugh F. Durrant-Whyte,et al.  A solution to the simultaneous localization and map building (SLAM) problem , 2001, IEEE Trans. Robotics Autom..

[20]  Ben J. A. Kröse,et al.  Trajectory Reconstruction for Self-Localization and Map Building , 2002, ICRA.

[21]  Roger J. Clarke,et al.  Digital compression of still images and video , 1995 .

[22]  Wolfram Burgard,et al.  A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots , 1998, Auton. Robots.

[23]  John J. Leonard,et al.  Adaptive Mobile Robot Navigation and Mapping , 1999, Int. J. Robotics Res..

[24]  Ben J. A. Kröse,et al.  Auxiliary particle filter robot localization from high-dimensional sensor observations , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[25]  A. Leonardis,et al.  Robust localization using eigenspace of spinning-images , 2000, Proceedings IEEE Workshop on Omnidirectional Vision (Cat. No.PR00704).

[26]  Patric Jensfelt,et al.  Active global localization for a mobile robot using multiple hypothesis tracking , 2001, IEEE Trans. Robotics Autom..

[27]  Bernt Schiele,et al.  Position estimation using principal components of range data , 1998, Robotics Auton. Syst..

[28]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[29]  Panos E. Trahanias,et al.  Closing multiple loops while mapping features in cyclic environments , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[30]  Ben J. A. Kröse,et al.  Efficient Greedy Learning of Gaussian Mixture Models , 2003, Neural Computation.

[31]  Patric Jensfelt,et al.  An experimental comparison of localisation methods, the MHL sessions , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[32]  Roland Siegwart,et al.  Feature-based multi-hypothesis localization and tracking for mobile robots using geometric constraints , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[33]  Stergios I. Roumeliotis,et al.  Appearance-based minimalistic metric SLAM , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[34]  Ben J. A. Krse,et al.  On the Use of Disparity Maps for robust Robot Localization under Different Illumination Conditions , 2003 .