Fast incremental square root information smoothing

Authors:
Michael Kaess;Ananth Ranganathan;Frank Dellaert
Affiliations:
Center for Robotics and Intelligent Machines, College of Computing, Georgia Institute of Technology, Atlanta, GA;Center for Robotics and Intelligent Machines, College of Computing, Georgia Institute of Technology, Atlanta, GA;Center for Robotics and Intelligent Machines, College of Computing, Georgia Institute of Technology, Atlanta, GA
Venue:
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Year:
2007

Citing 3
Cited 4

Spectral Partitioning for Structure from Motion

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Thin junction tree filters for simultaneous localization and mapping

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A multilevel relaxation algorithm for simultaneous localization and mapping

IEEE Transactions on Robotics

Online Sparse Matrix Gaussian Process Regression and Vision Applications

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
A collection of outdoor robotic datasets with centimeter-accuracy ground truth

Autonomous Robots
Normalized graph cuts for visual SLAM

IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
iSAM2: Incremental smoothing and mapping using the Bayes tree

International Journal of Robotics Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a novel approach to the problem of simultaneous localization and mapping (SLAM) based on incremental smoothing, that is suitable for real-time applications in large-scale environments. The main advantages over filter-based algorithms are that we solve the full SLAM problem without the need for any approximations, and that we do not suffer from linearization errors. We achieve efficiency by updating the square-root information matrix, a factored version of the naturally sparse smoothing information matrix. We can efficiently recover the exact trajectory and map at any given time by back-substitution. Furthermore, our approach allows access to the exact covariances, as it does not suffer from under-estimation of uncertainties, which is another problem inherent to filters. We present simulation-based results for the linear case, showing constant time updates for exploration tasks. We further evaluate the behavior in the presence of loops, and discuss how our approach extends to the non-linear case. Finally, we evaluate the overall non-linear algorithm on the standard Victoria Park data set.