Informative Path Planning for Location Fingerprint Collection
This work addresses the time-consuming site survey problem for fingerprint-based indoor localization, offering incremental improvements in path planning efficiency.
The paper tackles the problem of efficiently collecting location fingerprint data for indoor localization by proposing informative path planning algorithms under a budget constraint, showing that their greedy and genetic algorithms achieve higher utility and lower localization errors compared to state-of-the-art orienteering extensions in experiments across two indoor environments.
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 to collect fingerprint data from the target areas. In this paper, we consider the problem of informative path planning (IPP) to find the optimal walk for 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 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. We show through experimental data collected from two indoor environments with different characteristics that the two algorithms have low computation complexity, can generally achieve higher utility and lower localization errors compared to the extension of two state-of-the-art approaches to OP.