Elements of information theory
Elements of information theory
Frontier-based exploration using multiple robots
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Learning Occupancy Grid Maps with Forward Sensor Models
Autonomous Robots
Fast and accurate SLAM with Rao-Blackwellized particle filters
Robotics and Autonomous Systems
Assistive navigation of a robotic wheelchair using a multihierarchical model of the environment
Integrated Computer-Aided Engineering
Active mobile robot localization
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
IEEE Transactions on Robotics
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
An Entropy Optimization Strategy for Simultaneous Localization and Mapping
Journal of Intelligent and Robotic Systems
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Rao-Blackwellized particle filters (RBPFs) are animplementation of sequential Bayesian filtering that has beensuccessfully applied to mobile robot simultaneous localization andmapping (SLAM) and exploration. Measuring the uncertainty of thedistribution estimated by a RBPF is required for tasks such asinformation gain-guided exploration or detecting loop closures innested loop environments. In this paper we propose a new measurethat takes the uncertainty in both the robot path and the map intoaccount. Our approach relies on the entropy of the expected map(EM) of the RBPF, a new variable built by integrating the maphypotheses from all of the particles. Unlike previous works thatuse the joint entropy of the RBPF for active exploration, ourproposal is better suited to detect opportunities to close loops, akey aspect to reduce the robot path uncertainty and consequently toimprove the quality of the maps being built. We provide atheoretical discussion and experimental results with real data thatsupport our claims.