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

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.