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Toward mutual information based place recognition

Summary


Gaurav Pandey, James R. McBride, Silvio Savarese and Ryan M. Eustice, Toward mutual information based place recognition. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 3185-3192, Hong Kong, China, June 2014.

Abstract

This paper reports on a novel mutual information (MI) based algorithm for robust place recognition. The proposed method provides a principled framework for fusing the complementary information obtained from 3D lidar and camera imagery for recognizing places within an a priori map of a dynamic environment. The visual appearance of the locations in the map can be significantly different due to changing weather, lighting conditions and dynamical objects present in the environment. Various 3D/2D features are extracted from the textured point clouds (scans) and each scan is represented as a collection of these features. For two scans acquired from the same location, the high value of MI between the features present in the scans indicates that the scans are captured from the same location. We use a non-parametric entropy estimator to estimate the true MI from the sparse marginal and joint histograms of the features extracted from the scans. Experimental results using seasonal datasets collected over several years are used to validate the robustness of the proposed algorithm.

Bibtex entry

@INPROCEEDINGS { gpandey-2014a,
    AUTHOR = { Gaurav Pandey and James R. McBride and Silvio Savarese and Ryan M. Eustice },
    TITLE = { Toward mutual information based place recognition },
    BOOKTITLE = { Proceedings of the IEEE International Conference on Robotics and Automation },
    YEAR = { 2014 },
    MONTH = { June },
    ADDRESS = { Hong Kong, China },
    PAGES = { 3185--3192 },
}