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We present a novel framework for global localization and guidedrelocalization of a vehicle in an unstructured environment. Compared toexisting methods, our pipeline does not rely on cues from urban fixtures (e.g.,lane markings, buildings), nor does it make assumptions that require thevehicle to be navigating on a road network. Instead, we achieve localization inboth urban and non-urban environments by robustly associating and registeringthe 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 ourgraph-based data association algorithm. Further, the guided relocalizationcapability of our pipeline mitigates drift inherent in odometry-basedlocalization after the initial global localization. We evaluate our pipeline ontwo publicly-available, real-world datasets to demonstrate its effectiveness atglobal localization in both non-urban and urban environments. The Katwijk BeachPlanetary Rover dataset is used to show our pipeline's ability to performaccurate global localization in unstructured environments. Demonstrations onthe KITTI dataset achieve an average pose error of 3.8m across all 35localization events on Sequence 00 when localizing in a reference map createdfrom aerial images. Compared to existing works, our pipeline is more generalbecause it can perform global localization in unstructured environments usingmaps built from different viewpoints.

Global Localization in Unstructured Environments using Semantic Object Maps Built from Various Viewpoints