|Global Localization Using Low-frequency Image-based Descriptor and Range Data-based Validation
Chansoo Park and Jae-Bok Song*
International Journal of Control, Automation, and Systems, vol. 16, no. 3, pp.1332-1340, 2018
Abstract : "In image-based global localization, a robot pose is estimated through image association when the robot
revisits a previously visited location on a map. Image association is typically performed using high-level local
features such as scale invariant feature transform (SIFT) and speeded up robust feature (SURF). However, these
methods suffer from false-positive association and high computational load to reject outliers. In this study, we
introduce a novel global localization method based on the proposed low-frequency image-based descriptor (LFID)
and laser range data. The image is first processed by reducing the range of luminance in the frequency domain.
Visual features are then extracted from the processed image through a kernel window. These visual features are
described as binary representation for fast association. Because this binary representation includes a spatial distribution
of features, it can minimize false-positive association. Nevertheless, false-positive association could occur
when scenes appear to be similar from different viewpoints. To address this problem, this study adopts a laser
rangefinder to validate the similarity of the place and reject false-positives from the scene recognition. Experimental
results confirm the effectiveness of the proposed scheme in actual indoor environments."
Global localization, low-frequency image-based descriptor, range data validation.