Toward a unified Bayesian approach to hybrid metric-topological SLAM
Toward a unified Bayesian approach to hybrid metric-topological SLAM
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DOI：
10.1109/tro.2008.918049
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发表时间：
2008-04-01
影响因子：
7.8
通讯作者：
Gonzalez, Javier
中科院分区：
文献类型：
--
作者：
Blanco, Jose-Luis;Fernandez-Madrigal, Juan-Antonio;Gonzalez, Javier
本文介绍了一种在大范围环境中追求稳健性和准确性的同时定位与地图绘制(SLAM)方法。像大多数关于SLAM的成功工作一样，我们使用贝叶斯过滤来提供概率估计，该估计可以处理测量、机器人姿势和地图中的不确定性。我们的方法是基于离散-连续混合状态空间中的机器人路径重构，该状态空间自然地结合了度规和拓扑图。与以前的工作不同，本文有两个基本特点：1)对问题的度量部分和拓扑部分使用了统一的贝叶斯推理方法；2)混合映射上的信任分布的解析公式，使我们能够比以前的工作更准确和有效地保持大空间中的空间不确定性。我们还描述了一个以实时操作为目标的实际实现。我们的想法已经在具有多个嵌套环路的大型环境(高达30 000 m(2)，一条2公里的机器人路径)上得到了有前景的实验结果的验证，这是其他方法难以适当管理的。
This paper introduces a new approach to simultaneous localization and mapping (SLAM) that pursues robustness and accuracy in large-scale environments. Like most successful works on SLAM, we use Bayesian filtering to provide a probabilistic estimation that can cope with uncertainty in the measurements, the robot pose, and the map. Our approach is based on the reconstruction of the robot path in a hybrid discrete-continuous state space, which naturally combines metric and topological maps. There are two fundamental characteristics that set this paper apart from previous ones: 1) the use of a unified Bayesian inference approach both for the metrical and the topological parts of the problem and 2) the analytical formulation of belief distributions over hybrid maps, which allows us to maintain the spatial uncertainty in large spaces more accurately and efficiently than in previous works. We also describe a practical implementation that aims for real-time operation. Our ideas have been validated by promising experimental results in large environments (up to 30 000 m(2), a 2 km robot path) with multiple nested loops, which could hardly be managed appropriately by other approaches.