Abstract Precise mapping of buried utilities is critical to managing massive urban underground infrastructure and preventing utility incidents. Most current research only focuses on generating such maps based on complete information of underground utilities. However, in real-world practice, it is rare that a full picture of buried utilities can be obtained for such mapping. Therefore, this paper explores the problem of generating maps from partial observations of a scene where the actual world is not fully observed. In particular, we focus on the problem of generating 2D/3D maps of buried utilities using a probabilistic based approach. This has the advantage that the method is generic and can be applied to various sources of utility detections, e.g. manhole observations, sensors, and existing records. In this paper, we illustrate our novel methods based on detections from manhole observations and sensor measurements. This paper makes the following new contributions. It is the first time that partial observations have been used to generate utility maps using optimization based approaches. It is the first time that such a large variety of utilities' properties have been considered, such as location, directions, type and size. Another novel contribution is that different kinds of connections are included to reflect the complex layout and structure of buried utilities. Finally, for the first time to the best of our knowledge, we have integrated utility detection, probability calculation, model formulation and map generation into a single framework. The proposed framework represents all detections using a common language of probability distributions and then formulates the mapping problem as an Integer Linear Programming (ILP) problem and the final map is generated based on the solution with the highest probability sum. The effectiveness of this system is evaluated on synthetic and real data using appropriate evaluation metrics.

中文翻译：

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来自部分观测的 3D 映射：实用程序映射的应用

摘要 埋地公用设施的精确测绘对于管理大规模城市地下基础设施和预防公用设施事故至关重要。当前的大多数研究仅侧重于基于地下公用设施的完整信息生成此类地图。然而，在现实世界的实践中，很少能通过这种映射获得埋地公用设施的全貌。因此，本文探讨了从未完全观察到实际世界的场景的部分观察中生成地图的问题。特别是，我们专注于使用基于概率的方法生成埋地公用设施的 2D/3D 地图的问题。这样做的优点是该方法是通用的并且可以应用于公用事业检测的各种来源，例如检修孔观察、传感器和现有记录。在本文中，我们根据检修孔观察和传感器测量的检测来说明我们的新方法。本文做出以下新贡献。这是第一次使用基于优化的方法使用部分观察来生成效用图。这是第一次考虑如此广泛的公用事业属性，例如位置、方向、类型和大小。另一个新颖的贡献是包括不同类型的连接，以反映埋地设施的复杂布局和结构。最后，据我们所知，我们第一次将效用检测、概率计算、模型制定和地图生成集成到一个框架中。所提出的框架使用概率分布的通用语言表示所有检测，然后将映射问题表述为整数线性规划 (ILP) 问题，并根据具有最高概率和的解决方案生成最终映射。该系统的有效性是使用适当的评估指标在合成数据和真实数据上进行评估的。