Sparse matrices in matlab: design and implementation
SIAM Journal on Matrix Analysis and Applications
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Fast and accurate SLAM with Rao-Blackwellized particle filters
Robotics and Autonomous Systems
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Particle Filter SLAM with High Dimensional Vehicle Model
Journal of Intelligent and Robotic Systems
Consistent, convergent, and constant-time SLAM
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
SLAM in O(log n) with the combined Kalman - information filter
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Exactly Sparse Delayed-State Filters for View-Based SLAM
IEEE Transactions on Robotics
Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM
IEEE Transactions on Robotics
Sparse Local Submap Joining Filter for Building Large-Scale Maps
IEEE Transactions on Robotics
Divide and Conquer: EKF SLAM in
IEEE Transactions on Robotics
iSAM: Incremental Smoothing and Mapping
IEEE Transactions on Robotics
Linear-time robot localization and pose tracking using matching signatures
Robotics and Autonomous Systems
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In this paper we describe the Combined Kalman-Information Filter SLAM algorithm (CF SLAM), a judicious combination of Extended Kalman (EKF) and Extended Information Filters (EIF) that can be used to execute highly efficient SLAM in large environments. CF SLAM is always more efficient than any other EKF or EIF algorithm: filter updates can be executed in as low as O(logn) as compared with O(n^2) for Map Joining SLAM, O(n) for Divide and Conquer (D&C) SLAM, and the Sparse Local Submap Joining Filter (SLSJF). In the worst cases, updates are executed in O(n) for CF SLAM as compared with O(n^2) for all others. We also study an often overlooked problem in computationally efficient SLAM algorithms: data association. In situations in which only uncertain geometrical information is available for data association, CF SLAM is as efficient as D&C SLAM, and much more efficient than Map Joining SLAM or SLSJF. If alternative information is available for data association, such as texture in visual SLAM, CF SLAM outperforms all other algorithms. In large scale situations, both algorithms based on Extended Information filters, CF SLAM and SLSJF, avoid computing the full covariance matrix and thus require less memory, but still CF SLAM is the most computationally efficient. Both simulations and experiments with the Victoria Park dataset, the DLR dataset, and an experiment using visual stereo are used to illustrate the algorithms' advantages, also with respect to non filtering alternatives such as iSAM, the Treemap and Tectonic SAM.