Mapping with uncertainty representation is required in many research domains, such as localization and sensor fusion. Although there are many uncertainty explorations in pose estimation of an ego-robot with map information, the quality of the reference maps is often neglected. To avoid the potential problems caused by the errors of maps and a lack of the uncertainty quantification, an adequate uncertainty measure for the maps is required. In this paper, uncertain building models with abstract map surface using Gaussian Process (GP) is proposed to measure 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 the implicit GP map while local GP-block techniques are used as well. The proposed method is evaluated on LiDAR point clouds of city buildings collected by a mobile mapping system. Compared to the performances of other methods such like Octomap, Gaussian Process Occupancy Map (GPOM) and Bayersian Generalized Kernel Inference (BGKOctomap), our method has achieved higher Precision-Recall AUC for evaluated buildings.

翻译：在定位与传感器融合等众多研究领域中，需要具有不确定性表征的映射方法。尽管已有大量研究利用地图信息对自车位姿估计中的不确定性进行了探索，但参考地图的质量往往被忽视。为避免地图误差及缺乏不确定性量化可能带来的问题，需对地图建立恰当的不确定性度量。本文提出一种基于高斯过程(GP)的不确定建筑模型映射方法，利用抽象地图表面以概率方式度量地图不确定性。为减少简单平面对象的冗余计算，将高斯混合模型(GMM)提取的面与隐式GP地图相结合，并采用局部GP块技术。该方法在移动测绘系统采集的城市建筑激光雷达点云数据上进行了评估。与Octomap、高斯过程占据图(GPOM)及贝叶斯广义核推断(BGKOctomap)等其他方法相比，本方法在评估建筑上的精确率-召回率AUC指标更优。