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Parsimonious loop-closure detection based on global image-descriptors of panoramic images

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4 Author(s)
Lorenz Gerstmayr-Hillen ; Computer Engineering Group, Faculty of Technology, Bielefeld University, 33501 Bielefeld, Germany ; Oliver Schlüter ; Martin Krzykawski ; Ralf Möller

In the context of vision-based topological navigation, detecting loop closures requires to compare the robot's current camera image to a large number of images stored in the map. For efficient image comparisons, we apply distance functions to global image-descriptors, i.e. low-dimensional descriptors derived from the entire panoramic images. To identify promising combinations of descriptors and distance functions, we formulate the loop-closure detection as a binary classification problem and analyze the resulting receiver operator characteristics (ROC). The results of comparing a wide range of descriptors and distance functions reveal that reliable loop-closure detection is possible with a single 16- to 128-dimensional image-descriptor based on gray-value histograms or Fourier descriptors and that all considered distance functions have a comparable performance.

Published in:

Advanced Robotics (ICAR), 2011 15th International Conference on

Date of Conference:

20-23 June 2011