A Novel Measure of Uncertainty for Mobile Robot SLAM with Rao-Blackwellized Particle Filters
International Journal of Robotics Research
Interest Point Detectors for Visual SLAM
Current Topics in Artificial Intelligence
A Scalable Hybrid Multi-robot SLAM Method for Highly Detailed Maps
RoboCup 2007: Robot Soccer World Cup XI
Autonomous vision-based robotic exploration and mapping using hybrid maps and particle filters
Image and Vision Computing
Look-ahead Proposals for Robust Grid-based SLAM with Rao-Blackwellized Particle Filters
International Journal of Robotics Research
Robotic Mapping Using Measurement Likelihood Filtering
International Journal of Robotics Research
Contour Grouping Based on Contour-Skeleton Duality
International Journal of Computer Vision
Learning gas distribution models using sparse Gaussian process mixtures
Autonomous Robots
Stereo vision specific models for particle filter-based SLAM
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On measuring the accuracy of SLAM algorithms
Autonomous Robots
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Bridging the gap between feature- and grid-based SLAM
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An improvement on resampling algorithm of particle filters
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Optimal Filtering for Non-parametric Observation Models: Applications to Localization and SLAM
International Journal of Robotics Research
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Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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Information-theoretic compression of pose graphs for laser-based SLAM
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SC'12 Proceedings of the 2012 international conference on Spatial Cognition VIII
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Efficient grid-based spatial representations for robot navigation in dynamic environments
Robotics and Autonomous Systems
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International Journal of Robotics Research
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Recently, Rao-Blackwellized particle filters (RBPF) have been introduced as an effective means to solve the simultaneous localization and mapping problem. This approach uses a particle filter in which each particle carries an individual map of the environment. Accordingly, a key question is how to reduce the number of particles. In this paper, we present adaptive techniques for reducing this number in a RBPF for learning grid maps. We propose an approach to compute an accurate proposal distribution, taking into account not only the movement of the robot, but also the most recent observation. This drastically decreases the uncertainty about the robot's pose in the prediction step of the filter. Furthermore, we present an approach to selectively carry out resampling operations, which seriously reduces the problem of particle depletion. Experimental results carried out with real mobile robots in large-scale indoor, as well as outdoor, environments illustrate the advantages of our methods over previous approaches