Journal ArticleDOI
A multilevel relaxation algorithm for simultaneous localization and mapping
Udo Frese,P. Larsson,Tom Duckett +2 more
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TLDR
An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations, which has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops.Abstract:
This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping, because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling nonlinearities compared with other SLAM algorithms. Experimental comparisons with alternative algorithms using two well-known data sets and mapping results on a real robot are also presented.read more
Citations
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Journal ArticleDOI
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
TL;DR: In this article, the authors proposed an approach to compute an accurate proposal distribution, taking into account not only the movement of the robot, but also the most recent observation, which drastically decreases the uncertainty about the robot's pose in the prediction step of the filter.
Proceedings ArticleDOI
G 2 o: A general framework for graph optimization
TL;DR: G2o, an open-source C++ framework for optimizing graph-based nonlinear error functions, is presented and demonstrated that while being general g2o offers a performance comparable to implementations of state-of-the-art approaches for the specific problems.
Journal ArticleDOI
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Cesar Cadena,Luca Carlone,Henry Carrillo,Yasir Latif,Davide Scaramuzza,José L. Neira,Ian Reid,John J. Leonard +7 more
TL;DR: What is now the de-facto standard formulation for SLAM is presented, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers.
Journal ArticleDOI
A Tutorial on Graph-Based SLAM
TL;DR: An introductory description to the graph-based SLAM problem is provided and a state-of-the-art solution that is based on least-squares error minimization and exploits the structure of the SLAM problems during optimization is discussed.
Journal ArticleDOI
iSAM: Incremental Smoothing and Mapping
TL;DR: iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering and provides efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix.
References
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Sequential Monte Carlo methods in practice
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TL;DR: In this article, a discussion of some of the basic physical concepts and methods useful in the description of situations involving systems which consist of very many particulars is presented for the junior-senior thermodynamics course given in all departments as a standard part of the curriculum.