Christoffer Valgren, Tom Duckett and Achim J. Lilienthal

Incremental Spectral Clustering and Its Application to Topological Mapping

*Abstract:*
This paper presents a novel use of spectral clustering
algorithms to support cases where the entries in the affinity
matrix are costly to compute. The method is incremental – the
spectral clustering algorithm is applied to the affinity matrix
after each row/column is added – which makes it possible to
inspect the clusters as new data points are added. The method
is well suited to the problem of appearance-based, on-line
topological mapping for mobile robots. In this problem domain,
we show that we can reduce environment-dependent parameters
of the clustering algorithm to just a single, intuitive parameter.
Experimental results in large outdoor and indoor environments
show that we can close loops correctly by computing only a
fraction of the entries in the affinity matrix. The accompanying
video clip shows how an example map is produced by the
algorithm.

*Bibtex:*
@INPROCEEDINGS{Valgren_etal:ICRA:2007,

author = {C. Valgren and T. Duckett and A. J. Lilienthal},

title = {Incremental Spectral Clustering and Its Application to Topological Mapping},

booktitle = {Proc. IEEE Int. Conf. on Robotics and Automation},

year = {2007},

pages = {4283--4288}

}