Monte Carlo localization: efficient position estimation for mobile robots
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
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Towards a general theory of topological maps
Artificial Intelligence
The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures
International Journal of Robotics Research
DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Topological SLAM using neighbourhood information of places
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A multi-hypothesis topological SLAM approach for loop closing on edge-ordered graphs
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Estimating the absolute position of a mobile robot using position probability grids
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Pure topological mapping in mobile robotics
IEEE Transactions on Robotics
Online probabilistic topological mapping
International Journal of Robotics Research
IEEE Transactions on Robotics
Bayesian inference in the space of topological maps
IEEE Transactions on Robotics
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We present a unified filtering framework for hybrid metric/topological robot global localization and SLAM. At a high level, our method relies on a topological graph representation whose vertices define uniquely identifiable places in the environment and whose edges define feasible paths between them. At a low level, our method generalizes to any detailed metric submapping technique. The filtering framework we present is designed for multi-hypothesis estimation in order to account for ambiguity when closing loops and to account for uniform uncertainty when initializing pose estimates. Our implementation tests multiple topological hypotheses through the incremental construction of a hypothesis forest with each leaf representing a possible graph/state pair at the current time step. Instead of using a heuristic approach to accept or reject hypotheses, we propose a novel Bayesian method that computes the posterior probability of each hypothesis. In addition, for every topological hypothesis, a metric estimate is maintained with a local Kalman filter. Careful pruning of the hypothesis forest keeps the growing number of hypotheses under control while a garbage-collector hypothesis is used as a catch-all for pruned hypotheses. This enables the filter to recover from unmodeled disturbances such as the kidnapped robot problem.