Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters

作者：Giorgio Grisetti, Cyrill Stachniss, Wolfram Burgard · 发表于：IEEE Transactions on Robotics · 年份：2007 · DOI：10.1109/tro.2006.889486 · 被引用次数：2448 · 研究领域：Robotics and Sensor-Based Localization、Indoor and Outdoor Localization Technologies、Target Tracking and Data Fusion in Sensor Networks

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