Learning to Associate with CRF-Matching

This paper describes CRF-Matching, a recently proposed algorithm for data association of laser scans [19] that can be extended to handle many sensor modalities. CRF-Matching is a supervised probabilistic model able to jointly reason about the association of features. This is obtained by overcoming the independence assumption through the use of Conditional Random Fields (CRFs) [11]. CRFs are an extremely flexible technique for integrating different features in the same probabilistic framework. Features can be defined over different sensor modalities or designed to capture neighbourhood information. We present experimental results for image feature matching, and laser scan matching and compare those to standard techniques currently used for these tasks.

[1]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[2]  Jitendra Malik,et al.  Shape matching and object recognition using low distortion correspondences , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[3]  J. Besag.  Statistical Analysis of Non-Lattice Data , 1975 .

[4]  Michael I. Jordan,et al.  Loopy Belief Propagation for Approximate Inference: An Empirical Study , 1999, UAI.

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

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

[7]  James J. Little,et al.  Global localization using distinctive visual features , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .

[9]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[10]  Wolfram Burgard,et al.  CRF-Matching: Conditional Random Fields for Feature-Based Scan Matching , 2008 .

[11]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  C. Sammut,et al.  CASTER : A Robot for Urban Search and Rescue , 2005 .

[13]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[14]  Matthew Richardson,et al.  Markov logic networks , 2006, Machine Learning.

[15]  Dieter Fox,et al.  Voronoi Random Fields: Extracting Topological Structure of Indoor Environments via Place Labeling , 2007, IJCAI.

[16]  Dragomir Anguelov,et al.  SCAPE: shape completion and animation of people , 2005, ACM Trans. Graph..

[17]  Matthew A. Brown,et al.  Recognising panoramas , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[18]  Dieter Fox,et al.  A spatio-temporal probabilistic model for multi-sensor object recognition , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Evangelos E. Milios,et al.  Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Dieter Fox,et al.  A Spatio-Temporal Probabilistic Model for Multi-Sensor Multi-Class Object Recognition , 2007, ISRR.

[21]  Martial Hebert,et al.  Discriminative random fields: a discriminative framework for contextual interaction in classification , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[22]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[23]  Michael Ian Shamos,et al.  Computational geometry: an introduction , 1985 .