Single Cluster Graph Partitioning (SCGP) for Robotics Applications


We present an algorithm for finding a single cluster of well-connected nodes in a graph. The general problem is NP-hard, but our algorithm produces an approximate solution in $O(n^2)$ by considering the spectral properties of the graph's adjacency matrix. We show how this algorithm can be used to find sets of self-consistent hypotheses while rejecting incorrect hypotheses, a problem that frequently arises in robotics. We present results from a range-only SLAM system, a polynomial time data association algorithm, and a method for parametric line fitting that can outperform RANSAC.

This work was presented at the Robotics Science and Systems conference in June, 2005.

RSS 2005 Conference Materials


Edwin Olson eolson@mit.edAlumni 
Matthew Walter  Alumni 
John Leonard  Faculty 
Seth Teller  Faculty 

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