Abstract
This manuscript addresses the problem of optimization- based Simultaneous Localization and Mapping (SLAM), which is of concern when a robot, traveling in an unknown environment, has to build a world model, exploiting sensor measurements. Although the optimization problem underlying SLAM is nonlinear and nonconvex, related work showed that it is possible to compute an accurate linear approximation of the optimal solution for the case in which measurement covariance matrices have a block diagonal structure. In this paper we relax this hypothesis on the structure of measurement covariance and we propose a linear approximation that can deal with the general unstructured case. After presenting our theoretical derivation, we report an experimental evaluation of the proposed technique. The outcome confirms that the technique has remarkable advantages over state-of-the-art approaches and it is a promising solution for large-scale mapping.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Barooah, P., Hespanha, J.P.: Estimation on graphs from relative measurements. IEEE Control Systems Magazine 27(4), 57–74 (2007)
Carlone, L., Aragues, R., Castellanos, J.A., Bona, B.: A first-order solution to simultaneous localization and mapping with graphical models. In: Proc. of the IEEE lnternational Conf. on Robotics and Automation (2011)
Carlone, L., Aragues, R., Castellanos, J.A., Bona, B.: A linear approximation for graph-based simultaneous localization and mapping. In: Proc. of Robotics: Science and Systems (2011)
Carlone, L., Rosa, S., Yin, J.: Robotics research group: Resources – graph optimization with unstructured covariance (2012), www.polito.it/LabRob
Davis, T.A.: Direct Methods for Sparse Linear Systems. Fundamentals of Algorithms, vol. 2. Society for Industrial and Applied Mathematics, Philadelphia (2006) ISBN 0898716136
Dellaert, F., Carlson, J., Ila, V., Ni, K., Thorpe, C.: Subgraph-preconditioned conjugate gradients for large scale SLAM. In: Proc. of the IEEE-RSJ Int. Conf. on Intelligent Robots and Systems (2010)
Frese, U., Larsson, P., Duckett, T.: A multilevel relaxation algorithm for simultaneous localization and mapping. IEEE Trans. on Robotics 21(2), 196–207 (2005)
Grisetti, G., Stachniss, C., Burgard, W.: Non-linear constraint network optimization for efficient map learning. IEEE Trans. on Intelligent Transportation Systems 10(3), 428–439 (2009)
Konolige, K.: Large-scale map-making. In: Proc. of the AAAI National Conf. on Artificial Intelligence (2004)
Kümmerle, R., Steder, B., Dornhege, C., Ruhnke, M., Grisetti, G., Stachniss, C., Kleiner, A.: Slam benchmarking webpage (2009), http://ais.informatik.uni-freiburg.de/slamevaluation
Kummerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: G2o: A general framework for graph optimization. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 3607–3613 (May 2011), doi:10.1109/ICRA.2011.5979949
Lu, F., Milios, E.: Globally consistent range scan alignment for environment mapping. Autonomous Robots 4, 333–349 (1997)
Nocedal, J., Wright, S.J.: Numerical Optimization. Springer (2006)
Olson, E., Leonard, J.J., Teller, S.: Fast iterative optimization of pose graphs with poor initial estimates. In: Proc. of the IEEE Int. Conf. on Robotics and Automation, pp. 2262–2269 (2006)
Stachniss, C., Frese, U., Grisetti, G.: Open SLAM webpage (2007), http://openslam.org/
Sunderhauf, N., Protzel, N.: Towards a robust back-end for pose graph slam. In: Proc. of IEEE International Conference on Robotics and Automation, ICRA (2012)
Thrun, S., Montemerlo, M.: The GraphSLAM algorithm with applications to large-scale mapping of urban structures. Int. J. Robot. Res. 25, 403–429 (2006)
Thrun, S., Burgard, W., Fox, D.: Probabilistic robotics. MIT Press (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Carlone, L., Yin, J., Rosa, S., Yuan, Z. (2012). Graph Optimization with Unstructured Covariance: Fast, Accurate, Linear Approximation. In: Noda, I., Ando, N., Brugali, D., Kuffner, J.J. (eds) Simulation, Modeling, and Programming for Autonomous Robots. SIMPAR 2012. Lecture Notes in Computer Science(), vol 7628. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34327-8_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-34327-8_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34326-1
Online ISBN: 978-3-642-34327-8
eBook Packages: Computer ScienceComputer Science (R0)