Improved techniques for grid mapping with Rao-Blackwellized particle filters
Improved techniques for grid mapping with Rao-Blackwellized particle filters
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DOI：
10.1109/tro.2006.889486
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发表时间：
2007-02-01
影响因子：
7.8
通讯作者：
Burgard, Wolfram
中科院分区：
文献类型：
--
作者：
Grisetti, Giorgio;Stachniss, Cyrill;Burgard, Wolfram
最近，Rao-Blackwellized粒子滤波器（RBPF）被引入作为一种有效的手段来解决同时定位和映射问题。这种方法使用粒子过滤器，其中每个粒子都携带环境的单独地图。因此，一个关键问题是如何减少颗粒的数量。在本文中，我们提出了自适应技术，以减少这个数字在RBPF学习网格地图。我们提出了一种方法来计算一个准确的建议分布，不仅考虑到机器人的运动，而且最近的观察。这大大降低了过滤器预测步骤中机器人姿态的不确定性。此外，我们提出了一种方法，选择性地进行再循环操作，这严重减少了粒子耗尽的问题。与真实的移动的机器人在大规模的室内，以及室外环境进行的实验结果说明了我们的方法比以前的方法的优势。
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.