In this paper, we present a novel local feature detector
for the object recognition and robot navigation applications.
The proposed algorithm extracts highly robust and
repeatable features based on the key idea of tracking and
grouping multi-scale interest points and selecting a unique
representative structure with the strongest response in
both spatial and scale domains. Weighted Zernike moments
are used as the local descriptor for feature
representation. The experimental results and performance
evaluation show that our feature detector has high repeatability
and invariance to large scale, viewpoint and
illumination changes. The efficiency and usefulness of the
proposed feature detection method are also confirmed by
the excellent performance on object recognition and indoor
topological navigation.