A stochastic map for uncertain spatial relationships
Proceedings of the 4th international symposium on Robotics Research
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
FastSLAM: A Scalable Method for the Simultaneous Localization and Mapping Problem in Robotics (Springer Tracts in Advanced Robotics)
Visually Mapping the RMS Titanic: Conservative Covariance Estimates for SLAM Information Filters
International Journal of Robotics Research
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
International Journal of Robotics Research
Algorithm 887: CHOLMOD, Supernodal Sparse Cholesky Factorization and Update/Downdate
ACM Transactions on Mathematical Software (TOMS)
A multilevel relaxation algorithm for simultaneous localization and mapping
IEEE Transactions on Robotics
Exactly Sparse Delayed-State Filters for View-Based SLAM
IEEE Transactions on Robotics
FrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping
IEEE Transactions on Robotics
iSAM: Incremental Smoothing and Mapping
IEEE Transactions on Robotics
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This paper introduces an approach that reduces the size of the state and maximizes the sparsity of the information matrix in exactly sparse delayed-state SLAM. We propose constant time procedures to measure the distance between a given pair of poses, the mutual information gain for a given candidate link, and the joint marginals required for both measures. Using these measures, we can readily identify non redundant poses and highly informative links and use only those to augment and to update the state, respectively. The result is a delayed-state SLAM system that reduces both the use of memory and the execution time and that delays filter inconsistency by reducing the number of linearization introduced when adding new loop closure links. We evaluate the advantage of the proposed approach using simulations and data sets collected with real robots.