  The visual camera is an attractive device in beyond visual line of sight
(B-VLOS) drone operation, since they are low in size, weight, power, and cost,
and can provide redundant modality to GPS failures. However, state-of-the-art
visual localization algorithms are unable to match visual data that have a
significantly different appearance due to illuminations or viewpoints. This
paper presents iSimLoc, a condition/viewpoint consistent hierarchical global
re-localization approach. The place features of iSimLoc can be utilized to
search target images under changing appearances and viewpoints. Additionally,
our hierarchical global re-localization module refines in a coarse-to-fine
manner, allowing iSimLoc to perform a fast and accurate estimation. We evaluate
our method on one dataset with appearance variations and one dataset that
focuses on demonstrating large-scale matching over a long flight in complicated
environments. On our two datasets, iSimLoc achieves 88.7\% and 83.8\%
successful retrieval rates with 1.5s inferencing time, compared to 45.8% and
39.7% using the next best method. These results demonstrate robust localization
in a range of environments.
