Maximum likelihood estimation for multivariate mixture observations of Markov chins
IEEE Transactions on Information Theory
Learning dynamics: system identification for perceptually challenged agents
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Map learning with uninterpreted sensors and effectors
Artificial Intelligence
Learning Hidden Markov Models with Geometric Information
Learning Hidden Markov Models with Geometric Information
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Integrating grid-based and topological maps for mobile robot navigation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Integrating topological and metroc maps for mobile robot navigation: a statistical approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
Embedding robots into the Internet
Communications of the ACM
Self-Localization of Autonomous Robots by Hidden Representations
Autonomous Robots
A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
Fast, On-Line Learning of Globally Consistent Maps
Autonomous Robots
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Spatiotemporal Abstraction of Stochastic Sequential Processes
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Global localization and topological map-learning for robot navigation
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Exploring artificial intelligence in the new millennium
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Towards a general theory of topological maps
Artificial Intelligence
Learning and discovery of predictive state representations in dynamical systems with reset
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Journal of Intelligent and Robotic Systems
Predictive state representations: a new theory for modeling dynamical systems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Supervised semantic labeling of places using information extracted from sensor data
Robotics and Autonomous Systems
Omnidirectional Vision Based Topological Navigation
International Journal of Computer Vision
International Journal of Robotics Research
Appearance-based mapping using minimalistic sensor models
Autonomous Robots
Learning Network Topology from Simple Sensor Data
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Self-calibration of a vision-based sensor network
Image and Vision Computing
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Representing systems with hidden state
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Topological mapping with weak sensory data
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
Topological mapping through distributed, passive sensors
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Qualitative map learning based on co-visibility of objects
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Recognizing places using spectrally clustered local matches
Robotics and Autonomous Systems
Robot path planning in uncertain environments based on particle swarm optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Monte Carlo SLAM method for a small mobile robot with short-range sensors
MIC '08 Proceedings of the 27th IASTED International Conference on Modelling, Identification and Control
Simultaneous localization and mapping in dense environments
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Factoring the Mapping Problem: Mobile Robot Map-building in the Hybrid Spatial Semantic Hierarchy
International Journal of Robotics Research
Putting things in context: a topological approach to mapping contexts to ontologies
Journal on data semantics IX
Pure topological mapping in mobile robotics
IEEE Transactions on Robotics
Online probabilistic topological mapping
International Journal of Robotics Research
Learning Combinatorial Map Information from Permutations of Landmarks
International Journal of Robotics Research
Probabilistic multi-level maps from LIDAR data
International Journal of Robotics Research
Learning hidden Markov models with geometrical constraints
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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Topological maps provide a useful abstraction for robotic navigation and planning. Although stochastic maps can theoretically be learned using the Baum-Welch algorithm, without strong prior constraint on the structure of the model it is slow to converge, requires a great deal of data, and is often stuck in local minima. In this paper, we consider a special case of hidden Markov models for robot-navigation environments, in which states are associated with points in a metric configuration space. We assume that the robot has some odometric ability to measure relative transformations between its configurations. Such odometry is typically not precise enough to suffice for building a global map, but it does give valuable local information about relations between adjacent states. We present an extension of the Baum-Welch algorithm that takes advantage of this local odometric information, yielding faster convergence to better solutions with less data.