Autonomous Exploration: Driven by Uncertainty
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coverage for robotics – A survey of recent results
Annals of Mathematics and Artificial Intelligence
Coordination for Multi-Robot Exploration and Mapping
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Learning Occupancy Grid Maps with Forward Sensor Models
Autonomous Robots
Issues in the scaling of multi-robot systems for general problem solving
Autonomous Robots
Trajectory Optimization using Reinforcement Learning for Map Exploration
International Journal of Robotics Research
Autonomous vision-based robotic exploration and mapping using hybrid maps and particle filters
Image and Vision Computing
An Algorithm for Sensory Area Coverage by Mobile Robots Operating in Complex Arenas
Proceedings of the FIRA RoboWorld Congress 2009 on Advances in Robotics
Efficient optimization of information-theoretic exploration in SLAM
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Feature based occupancy grid maps for sonar based safe-mapping
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
3D mapping with multi-resolution occupied voxel lists
Autonomous Robots
An information-based exploration strategy for environment mapping with mobile robots
Robotics and Autonomous Systems
A multi-robot exploration algorithm based on a static Bluetooth communication chain
Robotics and Autonomous Systems
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Rolling dispersion for robot teams
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
A probabilistic framework for next best view estimation in a cluttered environment
Journal of Visual Communication and Image Representation
Local map-based exploration for mobile robots
Intelligent Service Robotics
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In this paper we introduce coverage maps as a new way of representing the environment of a mobile robot. Coverage maps store for each cell of a given grid a posterior about the amount the corresponding cell is covered by an obstacle. Using this representation a mobile robot can more accurately reason about its uncertainty in the map of the environment than with standard occupancy grids. We present a model for proximity sensors designed to update coverage maps upon sensory input. We also describe how coverage maps can be used to formulate a decision-theoretic approach for mobile robot exploration. We present experiments carried out with real robots in which accurate maps are build from noisy ultrasound data. Finally, we present a comparison of different view-point selection strategies for mobile robot exploration.