Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Elements of information theory
Elements of information theory
Autonomous Exploration: Driven by Uncertainty
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Bayesian Landmark Learning for Mobile Robot Localization
Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Coverage for robotics – A survey of recent results
Annals of Mathematics and Artificial Intelligence
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Coverage of Known Spaces: The Boustrophedon Cellular Decomposition
Autonomous Robots
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Stability analysis of mobile robot path tracking
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 3 - Volume 3
Learning probabilistic motion models for mobile robots
ICML '04 Proceedings of the twenty-first international conference on Machine learning
On visual maps and their automatic construction
On visual maps and their automatic construction
The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures
International Journal of Robotics Research
A relative map approach to SLAM based on shift and rotation invariants
Robotics and Autonomous Systems
Point-Based Value Iteration for Continuous POMDPs
The Journal of Machine Learning Research
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
International Journal of Robotics Research
Nonmyopic informative path planning in spatio-temporal models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Active learning with statistical models
Journal of Artificial Intelligence Research
Efficient planning of informative paths for multiple robots
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Exploring unknown environments with mobile robots using coverage maps
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Consistent, convergent, and constant-time SLAM
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Motion trajectory reproduction from generalized signature description
Pattern Recognition
Efficient optimization of information-theoretic exploration in SLAM
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Scalable and convergent multi-robot passive and active sensing
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
International Journal of Robotics Research
Reinforcement learning in robotics: A survey
International Journal of Robotics Research
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Automatically building maps from sensor data is a necessary and fundamental skill for mobile robots; as a result, considerable research attention has focused on the technical challenges inherent in the mapping problem. While statistical inference techniques have led to computationally efficient mapping algorithms, the next major challenge in robotic mapping is to automate the data collection process. In this paper, we address the problem of how a robot should plan to explore an unknown environment and collect data in order to maximize the accuracy of the resulting map. We formulate exploration as a constrained optimization problem and use reinforcement learning to find trajectories that lead to accurate maps. We demonstrate this process in simulation and show that the learned policy not only results in improved map building, but that the learned policy also transfers successfully to a real robot exploring on MIT campus.