Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Exploiting the architecture of dynamic systems
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Probabilistic Map Learning: Necessity and Difficulties
RUR '95 Proceedings of the International Workshop on Reasoning with Uncertainty in Robotics
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Exploring artificial intelligence in the new millennium
Thin Junction Tree Filtering for Simultaneous Localization and Mapping
Thin Junction Tree Filtering for Simultaneous Localization and Mapping
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Clustering without (thinking about) triangulation
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A Discussion of Simultaneous Localization and Mapping
Autonomous Robots
Distributed localization of networked cameras
Proceedings of the 5th international conference on Information processing in sensor networks
The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures
International Journal of Robotics Research
Robocentric map joining: Improving the consistency of EKF-SLAM
Robotics and Autonomous Systems
Fast and accurate SLAM with Rao-Blackwellized particle filters
Robotics and Autonomous Systems
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
A Study of the Rao-Blackwellised Particle Filter for Efficient and Accurate Vision-Based SLAM
International Journal of Computer Vision
Simultaneous Localization, Mapping and Moving Object Tracking
International Journal of Robotics Research
Distributed multirobot exploration, mapping, and task allocation
Annals of Mathematics and Artificial Intelligence
Junction Tree Factored Particle Inference Algorithm for Multi-Agent Dynamic Influence Diagrams
FAW '09 Proceedings of the 3d International Workshop on Frontiers in Algorithmics
Fast incremental square root information smoothing
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Nonlinear constraint network optimization for efficient map learning
IEEE Transactions on Intelligent Transportation Systems
Automatically and efficiently inferring the hierarchical structure of visual maps
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Large-scale loop-closing by fusing range data and aerial image
International Journal of Robotics and Automation
Factoring the Mapping Problem: Mobile Robot Map-building in the Hybrid Spatial Semantic Hierarchy
International Journal of Robotics Research
Lineage processing over correlated probabilistic databases
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Power-SLAM: a linear-complexity, anytime algorithm for SLAM
International Journal of Robotics Research
Faster Algorithms for Max-Product Message-Passing
The Journal of Machine Learning Research
LESS-mapping: Online environment segmentation based on spectral mapping
Robotics and Autonomous Systems
iSAM2: Incremental smoothing and mapping using the Bayes tree
International Journal of Robotics Research
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
Computer Vision and Image Understanding
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Simultaneous Localization and Mapping (SLAM) is a fundamental problem in mobile robotics: while a robot navigates in an unknown environment, it must incrementally build a map of its surroundings and, at the same time, localize itself within that map. One popular solution is to treat SLAM as an estimation problem and apply the Kalman filter; this approach is elegant, but it does not scale well: the size of the belief state and the time complexity of the filter update both grow quadratically in the number of landmarks in the map. This paper presents a filtering technique that maintains a tractable approximation of the belief state as a thin junction tree. The junction tree grows under filter updates and is periodically "thinned" via efficient maximum likelihood projections so inference remains tractable. When applied to the SLAM problem, these thin junction tree filters have a linear-space belief state and a linear-time filtering operation. Further approximation yields a filtering operation that is often constant-time. Experiments on a suite of SLAM problems validate the approach.