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：在许多研究领域（如定位与传感器融合）中，需要具备不确定性表示的映射。尽管利用地图信息进行自车姿态估计已存在诸多不确定性探索，但参考地图的质量常被忽视。为避免地图误差及缺乏不确定性量化可能引发的问题，需为地图提供充分的不确定性度量。本文提出了一种基于高斯过程（GP）的抽象地图表面不确定性建筑模型，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）提取的平面片与隐式GP地图相结合，同时采用局部GP块技术。所提方法在移动测绘系统采集的城市建筑激光雷达点云上进行了评估。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，本方法在评估建筑的精确率-召回率AUC指标上取得了更优表现。