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，需要对映射过程进行不确定性表征。尽管利用地图信息对自车进行位姿估计时已有很多关于不确定性的探索，但参考地图本身的质量常常被忽视。为避免地图误差及缺乏不确定性量化可能引发的问题，必须对地图采用恰当的不确定性度量。本文提出一种基于高斯过程的不确定建筑模型，利用抽象地图表面以概率方式度量地图不确定性。为减少对简单平面对象的冗余计算，将高斯混合模型提取的面元与隐式GP地图相结合，同时采用局部GP块技术。该方法在城市建筑激光雷达点云数据（由移动测绘系统采集）上进行了评估。与Octomap、高斯过程占据地图（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获得了更高的精确率-召回率AUC值。