On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
International Journal of Robotics Research
Thin junction tree filters for simultaneous localization and mapping
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Embedded trees: estimation of Gaussian Processes on graphs with cycles
IEEE Transactions on Signal Processing
On measuring the accuracy of SLAM algorithms
Autonomous Robots
Nonlinear constraint network optimization for efficient map learning
IEEE Transactions on Intelligent Transportation Systems
Information-based compact pose SLAM
IEEE Transactions on Robotics
Vast-scale Outdoor Navigation Using Adaptive Relative Bundle Adjustment
International Journal of Robotics Research
Mapping for the Support of First Responders in Critical Domains
Journal of Intelligent and Robotic Systems
iSAM2: Incremental smoothing and mapping using the Bayes tree
International Journal of Robotics Research
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Smoothing approaches to the Simultaneous Localization and Mapping (SLAM) problem in robotics are superior to the more common filtering approaches in being exact, better equipped to deal with non-linearities, and computing the entire robot trajectory. However, while filtering algorithms that perform map updates in constant time exist, no analogous smoothing method is available. We aim to rectify this situation by presenting a smoothingbased solution to SLAM using Loopy Belief Propagation (LBP) that can perform the trajectory and map updates in constant time except when a loop is closed in the environment. The SLAM problem is represented as a Gaussian Markov Random Field (GMRF) over which LBP is performed. We prove that LBP, in this case, is equivalent to Gauss-Seidel relaxation of a linear system. The inability to compute marginal covariances efficiently in a smoothing algorithm has previously been a stumbling block to their widespread use. LBP enables the efficient recovery of the marginal covariances, albeit approximately, of landmarks and poses. While the final covariances are overconfident, the ones obtained from a spanning tree of the GMRF are conservative, making them useful for data association. Experiments in simulation and using real data are presented.