Compact Signatures for High-speed Interest Point Description and Matching by Michael Calonder, Vincent Lepetit, Pascal Fua, Kurt Konolige, James Bowman, Patrick Mihelich. The abstract reads:
Prominent feature point descriptors such as SIFT and SURF allow reliable real-time matching but at a computational cost that limits the number of points that can be handled on PCs, and even more on less powerful mobile devices. A recently proposed technique that relies on statistical classiﬁcation to compute signatures has the potential to be much faster but at the cost of using very large amounts of memory, which makes it impractical for implementationon low-memory devices. In this paper, we show that we can exploit the sparseness of these signatures to compact them, speed up the computation, and drastically reduce memory usage. We base our approach on Compressive Sensing theory. We also highlight its effectiveness by incorporating it into two very different SLAM packages and demonstrating substantial performance increases.
Besides the obvious leap forward that compressive sensing allows for SLAM, here is something else I noted:
"...Remarkably, as will be shown in the experimental section, using either a Random Ortho-Projection or a PCA projection yields virtually the same results. This sheds light on the inner workings of the many methods , which perform PCA dimensionality reduction of SIFT-style descriptors. It suggests that their success owes more to the underlying sparsity of these descriptors than to PCA itself...."
If you do recall, this is the reason I wrote "Shouldn't We Call It "Sparse Sensing" or "Sparsity Sensing" instead ?". Slowly we are realizing that some empirical findings may be rooted in sparsity.
Michael Calonder's defended his PhD thesis in October 2010 Robust, High-speed Interest Point Matching For Real-Time Applications. Some of the folks who were involved in that paper work at Willow Garage, a very interesting company. An RSS feed for their news is here, their Twitter feed is here.