  We present SOS-Match, a novel framework for detecting and matching objects in
unstructured environments. Our system consists of 1) a front-end mapping
pipeline using a zero-shot segmentation model to extract object masks from
images and track them across frames and 2) a frame alignment pipeline that uses
the geometric consistency of object relationships to efficiently localize
across a variety of conditions. We evaluate SOS-Match on the Batvik seasonal
dataset which includes drone flights collected over a coastal plot of southern
Finland during different seasons and lighting conditions. Results show that our
approach is more robust to changes in lighting and appearance than classical
image feature-based approaches or global descriptor methods, and it provides
more viewpoint invariance than learning-based feature detection and description
approaches. SOS-Match localizes within a reference map up to 46x faster than
other feature-based approaches and has a map size less than 0.5% the size of
the most compact other maps. SOS-Match is a promising new approach for landmark
detection and correspondence search in unstructured environments that is robust
to changes in lighting and appearance and is more computationally efficient
than other approaches, suggesting that the geometric arrangement of segments is
a valuable localization cue in unstructured environments. We release our
datasets at https://acl.mit.edu/SOS-Match/.
