Abstract-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. Index Terms-Adaptive resampling, improved proposal, motion model, Rao-Blackwellized particle filter (RBPF), simultaneous localization and mapping (SLAM).
Furthermore, Rao-Blackwellized Particle Filters are more commonly used to solve the SLAM problem since they attempt to reduce the degeneracy and particle depletion problems presented by less advanced particle filters such as the Sequential Importance Sampling (SIS) and SIR filters REF .
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
IEEE Transactions on Robotics
IEEE Transactions on Robotics
Mathematics Computer Science