论文标题
使用GMM作为先验的不确定建筑模型的高斯过程映射
Gaussian Process Mapping of Uncertain Building Models with GMM as Prior
论文作者
论文摘要
在许多研究领域中，尤其是对于本地化，需要具有不确定性表示的映射。尽管有许多关于用图信息的自我 - 机器人姿势估计的不确定性的研究，但通常会忽略参考图的质量。为了避免由地图错误和缺乏不确定性定量引起的潜在问题，需要对地图进行足够的不确定性度量。在这封信中，提出了使用高斯过程（GPS）的抽象地图表面的不确定建筑模型以概率的方式描述地图不确定性。为了减少简单平面对象的冗余计算，将高斯混合物模型（GMM）提取的面与隐式GP映射相结合，也采用了局部GP-block技术。在移动映射系统收集的城市建筑物的LiDar Point云上评估了所提出的方法。与其他方法的性能相比，例如OCTOMAP，GP占用图（GPOM），Bayesian广义核Octomap（BGKOCTOMAP），本地自动相关性确定Hilbert Map（Lard-HM）和高斯隐式表面图（GPIS），我们的方法可实现更高的精确构建构建材料。
Mapping with uncertainty representation is required in many research domains, especially for localization. Although there are many investigations regarding the uncertainty of the pose estimation of an ego-robot with map information, the quality of the reference maps is often neglected. To avoid potential problems caused by the errors of maps and a lack of uncertainty quantification, an adequate uncertainty measure for the maps is required. In this letter, uncertain building models with abstract map surfaces using Gaussian Processes (GPs) are proposed to describe the map uncertainty in a probabilistic way. To reduce the redundant computation for simple planar objects, extracted facets from a Gaussian Mixture Model (GMM) are combined with an implicit GP map, also employing local GP-block techniques. The proposed method is evaluated on LiDAR point clouds of city buildings collected by a mobile mapping system. Compared to the performance of other methods such as OctoMap, GP Occupancy Map (GPOM), Bayesian Generalized Kernel OctoMap (BGKOctoMap), Local automatic relevance determination Hilbert map (LARD-HM) and Gaussian Implicit Surface map (GPIS), our method achieves a higher Precision-Recall AUC for the evaluated buildings.
