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,例如地方化和感应聚合领域,需要绘制具有不确定性的地图。虽然在对带有地图信息的自利机器人作出估计方面有许多不确定的勘探,但参考地图的质量往往被忽视。为了避免地图错误和缺乏不确定性量化而造成的潜在问题,需要对地图进行充分的不确定性测量。在本文件中,建议使用高山进程(GP)来以概率方式测量地图的抽象地图表面的不确定建筑模型。为了减少简单的平面物体的冗余计算,从高山混合模型(GMM)中提取的方块与隐含的GP地图相结合,同时使用当地GP块技术。建议的方法是在移动绘图系统收集的城市建筑物的LIDAR点云上进行评估。与诸如奥克托马普、高山进程奥库斯侵蚀图(GPOM)和巴耶斯通用开氏二次曲线图(BGKOctomap)等其他方法的性能相比,我们的方法已经达到了更高的对建筑物进行精确检索的AUC。</s>