  We present a novel framework for global localization and guided
relocalization of a vehicle in an unstructured environment. Compared to
existing methods, our pipeline does not rely on cues from urban fixtures (e.g.,
lane markings, buildings), nor does it make assumptions that require the
vehicle to be navigating on a road network. Instead, we achieve localization in
both urban and non-urban environments by robustly associating and registering
the vehicle's local semantic object map with a compact semantic reference map,
potentially built from other viewpoints, time periods, and/or modalities.
Robustness to noise, outliers, and missing objects is achieved through our
graph-based data association algorithm. Further, the guided relocalization
capability of our pipeline mitigates drift inherent in odometry-based
localization after the initial global localization. We evaluate our pipeline on
two publicly-available, real-world datasets to demonstrate its effectiveness at
global localization in both non-urban and urban environments. The Katwijk Beach
Planetary Rover dataset is used to show our pipeline's ability to perform
accurate global localization in unstructured environments. Demonstrations on
the KITTI dataset achieve an average pose error of 3.8m across all 35
localization events on Sequence 00 when localizing in a reference map created
from aerial images. Compared to existing works, our pipeline is more general
because it can perform global localization in unstructured environments using
maps built from different viewpoints.
