Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
An Experimental System for Incremental Environment Modelling by an Autonomous Mobile Robot
The First International Symposium on Experimental Robotics I
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
Thin Junction Tree Filtering for Simultaneous Localization and Mapping
Thin Junction Tree Filtering for Simultaneous Localization and Mapping
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
Treemap: an O(log n) algorithm for simultaneous localization and mapping
SC'04 Proceedings of the 4th international conference on Spatial Cognition: reasoning, Action, Interaction
A multilevel relaxation algorithm for simultaneous localization and mapping
IEEE Transactions on Robotics
Subjective local maps for hybrid metric-topological SLAM
Robotics and Autonomous Systems
Experiments with Cooperative Control of Underwater Robots
International Journal of Robotics Research
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
Recognizing places using spectrally clustered local matches
Robotics and Autonomous Systems
Sparsing of information matrix for practical application of a robot's slam
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A stochastically stable solution to the problem of robocentric mapping
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Information-based compact pose SLAM
IEEE Transactions on Robotics
Robotics and Autonomous Systems
Smoothing-based submap merging in large area SLAM
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Laser and Radar Based Robotic Perception
Foundations and Trends in Robotics
A review of path planning and mapping technologies for autonomous mobile robot systems
Proceedings of the 5th ACM COMPUTE Conference: Intelligent & scalable system technologies
A Kalman filter based approach to probabilistic gas distribution mapping
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Semi-parametric learning for visual odometry
International Journal of Robotics Research
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Recent research concerning the Gaussian canonical form for Simultaneous Localization and Mapping (SLAM) has given rise to a handful of algorithms that attempt to solve the SLAM scalability problem for arbitrarily large environments. One such estimator that has received due attention is the Sparse Extended Information Filter (SEIF) proposed by Thrun et al., which is reported to be nearly constant time, irrespective of the size of the map. The key to the SEIF's scalability is to prune weak links in what is a dense information (inverse covariance) matrix to achieve a sparse approximation that allows for efficient, scalable SLAM. We demonstrate that the SEIF sparsification strategy yields error estimates that are overconfident when expressed in the global reference frame, while empirical results show that relative map consistency is maintained. In this paper, we propose an alternative scalable estimator based on an information form that maintains sparsity while preserving consistency. The paper describes a method for controlling the population of the information matrix, whereby we track a modified version of the SLAM posterior, essentially by ignoring a small fraction of temporal measurements. In this manner, the Exactly Sparse Extended Information Filter (ESEIF) performs inference over a model that is conservative relative to the standard Gaussian distribution. We compare our algorithm to the SEIF and standard EKF both in simulation as well as on two nonlinear datasets. The results convincingly show that our method yields conservative estimates for the robot pose and map that are nearly identical to those of the EKF.