Gaussian process occupancy maps
Gaussian process occupancy maps
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DOI：
10.1177/0278364911421039
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发表时间：
2012-01-01
影响因子：
9.2
通讯作者：
Ramos, Fabio T.
中科院分区：
文献类型：
--
作者：
O'Callaghan, Simon T.;Ramos, Fabio T.
我们介绍了一种新的统计建模技术，用于建筑占用地图。映射的问题被解决作为一个分类任务，其中机器人的环境被分类为区域的占用和自由空间。这是通过采用修改后的高斯过程作为非参数贝叶斯学习技术来利用现实世界环境固有的结构这一事实来获得的。这种结构引入了地图上的点之间的依赖关系，这是许多常见的地图绘制技术（如占用网格）所不考虑的。我们的方法是一个“随时”的算法，能够生成精确的表示在任意分辨率的大型环境，以适应许多应用程序。它还提供了相关的变化到闭塞区域和传感器光束之间的推断，即使相对较少的观察。至关重要的是，该技术可以处理可能来自多个来源的噪音数据，并将其融合到机器人周围环境的强大通用概率表示中。我们展示了我们的方法在模拟数据集上的好处，这些数据集具有已知的地面实况和室外城市环境。
We introduce a new statistical modelling technique for building occupancy maps. The problem of mapping is addressed as a classification task where the robot's environment is classified into regions of occupancy and free space. This is obtained by employing a modified Gaussian process as a non-parametric Bayesian learning technique to exploit the fact that real-world environments inherently possess structure. This structure introduces dependencies between points on the map which are not accounted for by many common mapping techniques such as occupancy grids. Our approach is an 'anytime' algorithm that is capable of generating accurate representations of large environments at arbitrary resolutions to suit many applications. It also provides inferences with associated variances into occluded regions and between sensor beams, even with relatively few observations. Crucially, the technique can handle noisy data, potentially from multiple sources, and fuse it into a robust common probabilistic representation of the robot's surroundings. We demonstrate the benefits of our approach on simulated datasets with known ground truth and in outdoor urban environments.