A stochastic map for uncertain spatial relationships
Proceedings of the 4th international symposium on Robotics Research
Introduction to algorithms
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
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)
Using covariance intersection for SLAM
Robotics and Autonomous Systems
D-SLAM: A Decoupled Solution to Simultaneous Localization and Mapping
International Journal of Robotics Research
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
Exactly Sparse Extended Information Filters for Feature-based SLAM
International Journal of Robotics Research
Detecting Loop Closure with Scene Sequences
International Journal of Computer Vision
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Algorithm 887: CHOLMOD, Supernodal Sparse Cholesky Factorization and Update/Downdate
ACM Transactions on Mathematical Software (TOMS)
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Divide and Conquer: EKF SLAM in
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
Action selection for single-camera SLAM
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robotics and Autonomous Systems
Robotics and Autonomous Systems
Information-theoretic compression of pose graphs for laser-based SLAM
International Journal of Robotics Research
Fast topology estimation for image mosaicing using adaptive information thresholding
Robotics and Autonomous Systems
A fast vision system for soccer robot
Applied Bionics and Biomechanics - Personal Care Robotics
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Pose SLAMis the variant of simultaneous localization and map building (SLAM) is the variant of SLAM, in which only the robot trajectory is estimated and where landmarks are only used to produce relative constraints between robot poses. To reduce the computational cost of the information filter form of PoseSLAM and, at the same time, to delay inconsistency as much as possible, we introduce an approach that takes into account only highly informative loop-closure links and nonredundant poses. This approach includes constant time procedures to compute the distance between poses, the expected information gain for each potential link, and the exact marginal covariances while moving in open loop, as well as a procedure to recover the state after a loop closure that, in practical situations, scales linearly in terms of both time and memory. Using these procedures, the robot operates most of the time in open loop, and the cost of the loop closure is amortized over long trajectories. This way, the computational bottleneck shifts to data association, which is the search over the set of previously visited poses to determine good candidates for sensor registration. To speed up data association, we introduce a method to search for neighboring poses whose complexity ranges from logarithmic in the usual case to linear in degenerate situations. The method is based on organizing the pose information in a balanced tree whose internal levels are defined using interval arithmetic. The proposed Pose-SLAM approach is validated through simulations, real mapping sessions, and experiments using standard SLAM data sets.