Elements of information theory
Elements of information theory
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
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
Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association
Fast and accurate SLAM with Rao-Blackwellized particle filters
Robotics and Autonomous Systems
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments
IEEE Transactions on Robotics
Bayesian inference in the space of topological maps
IEEE Transactions on Robotics
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
IEEE Transactions on Robotics
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In this work we address the problem of optimal Bayesian filtering for dynamic systems with observation models that cannot be approximated properly as any parameterized distribution. In the context of mobile robots this problem arises in localization and simultaneous localization and mapping (SLAM) with occupancy grid maps. The lack of a parameterized observation model for these maps forces a sample-based representation, commonly through Monte Carlo methods for sequential filtering, also called particle filters. Our work is grounded on the demonstrated existence of an optimal proposal distribution for particle filters. However, this optimal distribution is not directly applicable to systems with non-parametric models. By integrating ideas from previous works on adaptive sample size, auxiliary particle filters, and rejection sampling, we derive a new particle filter algorithm that enables the usage of the optimal proposal to estimate the true posterior density of a non-parametric dynamic system. This new filter is better suited, both theoretically and in practice, than previous approximate methods for indoor and outdoor localization and SLAM, as confirmed by experiments with real robots.