On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
D-SLAM: A Decoupled Solution to Simultaneous Localization and Mapping
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
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)
Information-based compact pose SLAM
IEEE Transactions on Robotics
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments
IEEE Transactions on Robotics
Exactly Sparse Delayed-State Filters for View-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
Large-Scale 6-DOF SLAM With Stereo-in-Hand
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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The computational bottleneck in all information-based algorithms for simultaneous localization and mapping (SLAM) is the recovery of the state mean and covariance. The mean is needed to evaluate model Jacobians and the covariance is needed to generate data association hypotheses. In general, recovering the state mean and covariance requires the inversion of a matrix with the size of the state, which is computationally too expensive in time and memory for large problems. Exactly sparse state representations, such as that of Pose SLAM, alleviate the cost of state recovery either in time or in memory, but not in both. In this paper, we present an approach to state estimation that is linear both in execution time and in memory footprint at loop closure, and constant otherwise. The method relies on a state representation that combines the Kalman and the information-based approaches. The strategy is valid for any SLAM system that maintains constraints between marginal states at different time slices. This includes both Pose SLAM, the variant of SLAM where only the robot trajectory is estimated, and hierarchical techniques in which submaps are registered with a network of relative geometric constraints.