Recently, many vision-based robotic applications
such as visual SLAM (Simultaneous Localization And Mapping)
and autonomous navigation have achieved good performance
using visual features. In these applications, robust feature
tracking plays an important role, e.g., in scene recognition
for autonomous navigation and in data association for visual
SLAM. In this paper, we propose a hierarchical outlier detection
algorithm for robust feature tracking; the algorithm uses a
simple window-based correlation (NCC) and enforces angular
and scale constraints. The proposed algorithm maximizes the
inter-cluster score and detects outliers that do not satisfy
the angular constraints. The remaining outliers are detected
by enforcing scale constraints using SIFT descriptors. The
proposed algorithm is efficient and particularly useful for
scene recognition, in which an image corresponding to a query
image is searched among data images. Experimental results
demonstrate that the proposed algorithm is robust to outliers
and image variations such as scale changes. One of the main
applications of the proposed algorithm is global localization due
to its low computational complexity and robustness to outliers.