Fingerprint-based indoor localization methods are promising due to the high availability of deployed access points and compatibility with commercial off-the-shelf user devices. However, to train regression models for localization, an extensive site survey is required, which collects fingerprint data from the target areas. In this paper, we consider the problem of informative path planning (IPP) to find the optimal walk for a site survey subject to a budget constraint. IPP for location fingerprint collection is related to the well-known orienteering problem (OP) but is more challenging due to its edge-based non-additive rewards and revisits. Given the NP-hardness of IPP, we propose two heuristic approaches: a Greedy algorithm and a Genetic algorithm. Through experimental data collected from two indoor environments with different characteristics, we show that the two algorithms have low computation complexity, and can generally achieve a higher utility, as well as lower localization errors compared to the extension of two state-of-the-art approaches to OP.

中文翻译：

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位置指纹采集的信息路径规划

由于已部署接入点的高可用性以及与商用现成用户设备的兼容性，基于指纹的室内定位方法很有前景。但是，要训练用于定位的回归模型，需要进行广泛的现场调查，从目标区域收集指纹数据。在本文中，我们考虑了信息路径规划 (IPP) 问题，以找到受预算约束的站点调查的最佳步行。用于位置指纹收集的 IPP 与众所周知的定向运动问题 (OP) 相关，但由于其基于边缘的非加性奖励和重访而更具挑战性。鉴于 IPP 的 NP-hardness，我们提出了两种启发式方法：贪心算法和遗传算法。通过从两个不同特性的室内环境中收集的实验数据，