  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.
