iSAM: Fast Incremental Smoothing and Mapping with Efficient Data Association

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/38369

Title: iSAM: Fast Incremental Smoothing and Mapping with Efficient Data Association
Author: Kaess, Michael ; Ranganathan, Ananth ; Dellaert, Frank
Abstract: We introduce incremental smoothing and mapping (iSAM), a novel approach to the problem of simultaneous localization and mapping (SLAM) that addresses the data association problem and allows real-time application in large-scale environments. We employ smoothing to obtain the complete trajectory and map without the need for any approximations, exploiting the natural sparsity of the smoothing information matrix. A QR-factorization of this information matrix is at the heart of our approach. It provides efficient access to the exact covariances as well as to conservative estimates that are used for online data association. It also allows recovery of the exact trajectory and map at any given time by backsubstitution. Instead of refactoring in each step, we update the QR-factorization whenever a new measurement arrives. We analyze the effect of loops, and show how our approach extends to the non-linear case. Finally, we provide experimental validation of the overall non-linear algorithm based on the standard Victoria Park data set with unknown correspondences.
Description: ©2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Presented at the 2007 IEEE International Conference on Robotics and Automation (ICRA), 10-14 April 2007, Roma, Italy. DOI: 10.1109/ROBOT.2007.363563
Type: Post-print
Proceedings
URI: http://hdl.handle.net/1853/38369
ISSN: 1050-4729
Citation: Kaess, M., Ranganathan, A., & Dellaert, F. (2007). “iSAM: Fast Incremental Smoothing and Mapping with Efficient Data Association”. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2007), 10-14 April 2007, 1670-1677.
Date: 2007-04
Contributor: Georgia Institute of Technology. Center for Robotics and Intelligent Machines
Georgia Institute of Technology. College of Computing
Publisher: Georgia Institute of Technology
Institute of Electrical and Electronics Engineers
Subject: Data association
Incremental smoothing and mapping
QR factorization
Simultaneous localization and mapping

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