Variable reordering strategies for SLAM

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2012

PDF thumbnail
(PDF, 524.3 KB )


State of the art methods for state estimation and perception make use of least-squares optimization methods to perform efficient inference on noisy sensor data. Much of this efficiency is achieved by using sparse matrix factorization methods. The sparsity structure of the underlying matrix factorization which makes these optimization methods tractable is highly dependent on the choice of variable reordering; but there has been no systematic evaluation of reordering methods in the SLAM community. In this paper we evaluate the performance of various reordering techniques on benchmark SLAM data sets and provide definitive recommendations based on our results. We also compare these state of the art algorithms against our simple and easy to implement algorithm which achieves comparable performance. Finally, we provide empirical evidence that few gains remain with respect to variants of minimum degree ordering.


    TITLE      = {Variable reordering strategies for SLAM},
    AUTHOR     = {Pratik Agarwal and Edwin Olson},
    BOOKTITLE  = {Proceedings of the {IEEE/RSJ} International Conference on Intelligent
                 Robots and Systems {(IROS)}},
    YEAR       = {2012},
    MONTH      = {October},
    KEYWORDS   = {SLAM, matrix factorization},