Characterisation of carbon nano-onions using Raman spectroscopy

Characteristics of the Raman spectrum from carbon onions have been identified in terms of the position of the G peak and appearance of the transverse optic phonon peaks. Five new peaks were observed in the low wavenumber region, at about 1100, 861, 700, 450 and 250 cm(-1). The origins of these peaks are discussed in terms of the phonon density of states (PDOS) and phonon dispersion curves of graphite. The curvature of the graphene planes is invoked to explain the relaxation of the Raman selection rules and the appearance of the new peaks. The Raman spectrum of carbon onions is compared with that of highly oriented pyrolytic graphite (HOPG). The strain of graphene planes due to curvature has been estimated analytically and is used to account for the downward shift of the G peak. (C) 2003 Elsevier Science B.V. All rights reserved.

Image-based localization and pose recovery using scale invariant features

In this paper, we propose a vision based mobile robot localization strategy. Local scale-invariant features are used as natural landmarks in unstructured and unmodified environment. The local characteristics of the features we use prove to be robust to occlusion and outliers. In addition, the invariance of the features to viewpoint change makes them suitable landmarks for mobile robot localization. Scale-invariant features detected in the first exploration are indexed into a location database. Indexing and voting allow efficient recognition of global localization. The localization result is verified by epipolar geometry between the representative view in database and the view to be localized, thus the probability of false localization will be decreased. The localization system can recover the pose of the camera mounted on the robot by essential matrix decomposition. Then the position of the robot can be computed easily. Both calibrated and un-calibrated cases are discussed and relative position estimation based on calibrated camera turns out to be the better choice. Experimental results show that our approach is effective and reliable in the case of illumination changes, similarity transformations and extraneous features. © 2004 IEEE.